**Algorithms**: A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.

**Programming, Linear**: A technique of operations research for solving certain kinds of problems involving many variables where a best value or set of best values is to be found. It is most likely to be feasible when the quantity to be optimized, sometimes called the objective function, can be stated as a mathematical expression in terms of the various activities within the system, and when this expression is simply proportional to the measure of the activities, i.e., is linear, and when all the restrictions are also linear. It is different from computer programming, although problems using linear programming techniques may be programmed on a computer.

**Cybernetics**: That branch of learning which brings together theories and studies on communication and control in living organisms and machines.

**Computer Simulation**: Computer-based representation of physical systems and phenomena such as chemical processes.

**Numerical Analysis, Computer-Assisted**: Computer-assisted study of methods for obtaining useful quantitative solutions to problems that have been expressed mathematically.

**Models, Theoretical**: Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.

**Computational Biology**: A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.

**Models, Biological**: Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.

**Software**: Sequential operating programs and data which instruct the functioning of a digital computer.

**Nonlinear Dynamics**: The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos.

**Pattern Recognition, Automated**: In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)

**Models, Statistical**: Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.

**Metabolic Networks and Pathways**: Complex sets of enzymatic reactions connected to each other via their product and substrate metabolites.

**Artificial Intelligence**: Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.

**Systems Biology**: Comprehensive, methodical analysis of complex biological systems by monitoring responses to perturbations of biological processes. Large scale, computerized collection and analysis of the data are used to develop and test models of biological systems.

**Image Processing, Computer-Assisted**: A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.

**Phantoms, Imaging**: Devices or objects in various imaging techniques used to visualize or enhance visualization by simulating conditions encountered in the procedure. Phantoms are used very often in procedures employing or measuring x-irradiation or radioactive material to evaluate performance. Phantoms often have properties similar to human tissue. Water demonstrates absorbing properties similar to normal tissue, hence water-filled phantoms are used to map radiation levels. Phantoms are used also as teaching aids to simulate real conditions with x-ray or ultrasonic machines. (From Iturralde, Dictionary and Handbook of Nuclear Medicine and Clinical Imaging, 1990)

**Signal Processing, Computer-Assisted**: Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.

**Image Enhancement**: Improvement of the quality of a picture by various techniques, including computer processing, digital filtering, echocardiographic techniques, light and ultrastructural MICROSCOPY, fluorescence spectrometry and microscopy, scintigraphy, and in vitro image processing at the molecular level.

**Reproducibility of Results**: The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.

**Image Interpretation, Computer-Assisted**: Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.

**Models, Genetic**: Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.

**Proteins**: Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein.

**Cluster Analysis**: A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.

**Gene Expression Profiling**: The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.

**Problem Solving**: A learning situation involving more than one alternative from which a selection is made in order to attain a specific goal.

**Social Problems**: Situations affecting a significant number of people, that are believed to be sources of difficulty or threaten the stability of the community, and that require programs of amelioration.

**Radiotherapy Planning, Computer-Assisted**: Computer-assisted mathematical calculations of beam angles, intensities of radiation, and duration of irradiation in radiotherapy.

**Drug Design**: The molecular designing of drugs for specific purposes (such as DNA-binding, enzyme inhibition, anti-cancer efficacy, etc.) based on knowledge of molecular properties such as activity of functional groups, molecular geometry, and electronic structure, and also on information cataloged on analogous molecules. Drug design is generally computer-assisted molecular modeling and does not include pharmacokinetics, dosage analysis, or drug administration analysis.

**Sensitivity and Specificity**: Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)

**Child Behavior Disorders**: Disturbances considered to be pathological based on age and stage appropriateness, e.g., conduct disturbances and anaclitic depression. This concept does not include psychoneuroses, psychoses, or personality disorders with fixed patterns.

**Metabolic Engineering**: Methods and techniques used to genetically modify cells' biosynthetic product output and develop conditions for growing the cells as BIOREACTORS.

**Models, Molecular**: Models used experimentally or theoretically to study molecular shape, electronic properties, or interactions; includes analogous molecules, computer-generated graphics, and mechanical structures.

**Chemistry, Pharmaceutical**: Chemistry dealing with the composition and preparation of agents having PHARMACOLOGIC ACTIONS or diagnostic use.

**Time Factors**: Elements of limited time intervals, contributing to particular results or situations.

**Equipment Design**: Methods of creating machines and devices.

**Industrial Microbiology**: The study, utilization, and manipulation of those microorganisms capable of economically producing desirable substances or changes in substances, and the control of undesirable microorganisms.

**Radiotherapy, Intensity-Modulated**: CONFORMAL RADIOTHERAPY that combines several intensity-modulated beams to provide improved dose homogeneity and highly conformal dose distributions.

**Biotechnology**: Body of knowledge related to the use of organisms, cells or cell-derived constituents for the purpose of developing products which are technically, scientifically and clinically useful. Alteration of biologic function at the molecular level (i.e., GENETIC ENGINEERING) is a central focus; laboratory methods used include TRANSFECTION and CLONING technologies, sequence and structure analysis algorithms, computer databases, and gene and protein structure function analysis and prediction.

**Monte Carlo Method**: In statistics, a technique for numerically approximating the solution of a mathematical problem by studying the distribution of some random variable, often generated by a computer. The name alludes to the randomness characteristic of the games of chance played at the gambling casinos in Monte Carlo. (From Random House Unabridged Dictionary, 2d ed, 1993)

**Bioreactors**: Tools or devices for generating products using the synthetic or chemical conversion capacity of a biological system. They can be classical fermentors, cell culture perfusion systems, or enzyme bioreactors. For production of proteins or enzymes, recombinant microorganisms such as bacteria, mammalian cells, or insect or plant cells are usually chosen.

**Technology, Pharmaceutical**: The application of scientific knowledge or technology to pharmacy and the pharmaceutical industry. It includes methods, techniques, and instrumentation in the manufacture, preparation, compounding, dispensing, packaging, and storing of drugs and other preparations used in diagnostic and determinative procedures, and in the treatment of patients.

**Combinatorial Chemistry Techniques**: A technology, in which sets of reactions for solution or solid-phase synthesis, is used to create molecular libraries for analysis of compounds on a large scale.

**Drug Discovery**: The process of finding chemicals for potential therapeutic use.

**Questionnaires**: Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.

**Signal-To-Noise Ratio**: The comparison of the quantity of meaningful data to the irrelevant or incorrect data.

**Neural Networks (Computer)**: A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.

**Medical Records, Problem-Oriented**: A system of record keeping in which a list of the patient's problems is made and all history, physical findings, laboratory data, etc. pertinent to each problem are placed under that heading.

**Imaging, Three-Dimensional**: The process of generating three-dimensional images by electronic, photographic, or other methods. For example, three-dimensional images can be generated by assembling multiple tomographic images with the aid of a computer, while photographic 3-D images (HOLOGRAPHY) can be made by exposing film to the interference pattern created when two laser light sources shine on an object.

**Models, Chemical**: Theoretical representations that simulate the behavior or activity of chemical processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.

**Fermentation**: Anaerobic degradation of GLUCOSE or other organic nutrients to gain energy in the form of ATP. End products vary depending on organisms, substrates, and enzymatic pathways. Common fermentation products include ETHANOL and LACTIC ACID.

**Temperature**: The property of objects that determines the direction of heat flow when they are placed in direct thermal contact. The temperature is the energy of microscopic motions (vibrational and translational) of the particles of atoms.

**Molecular Structure**: The location of the atoms, groups or ions relative to one another in a molecule, as well as the number, type and location of covalent bonds.

**Computer-Aided Design**: The use of computers for designing and/or manufacturing of anything, including drugs, surgical procedures, orthotics, and prosthetics.

**Structure-Activity Relationship**: The relationship between the chemical structure of a compound and its biological or pharmacological activity. Compounds are often classed together because they have structural characteristics in common including shape, size, stereochemical arrangement, and distribution of functional groups.

**High-Throughput Screening Assays**: Rapid methods of measuring the effects of an agent in a biological or chemical assay. The assay usually involves some form of automation or a way to conduct multiple assays at the same time using sample arrays.

**Culture Media**: Any liquid or solid preparation made specifically for the growth, storage, or transport of microorganisms or other types of cells. The variety of media that exist allow for the culturing of specific microorganisms and cell types, such as differential media, selective media, test media, and defined media. Solid media consist of liquid media that have been solidified with an agent such as AGAR or GELATIN.

**Molecular Sequence Data**: Descriptions of specific amino acid, carbohydrate, or nucleotide sequences which have appeared in the published literature and/or are deposited in and maintained by databanks such as GENBANK, European Molecular Biology Laboratory (EMBL), National Biomedical Research Foundation (NBRF), or other sequence repositories.

**Treatment Outcome**: Evaluation undertaken to assess the results or consequences of management and procedures used in combating disease in order to determine the efficacy, effectiveness, safety, and practicability of these interventions in individual cases or series.

**Radiation Dosage**: The amount of radiation energy that is deposited in a unit mass of material, such as tissues of plants or animal. In RADIOTHERAPY, radiation dosage is expressed in gray units (Gy). In RADIOLOGIC HEALTH, the dosage is expressed by the product of absorbed dose (Gy) and quality factor (a function of linear energy transfer), and is called radiation dose equivalent in sievert units (Sv).

**Radiometry**: The measurement of radiation by photography, as in x-ray film and film badge, by Geiger-Mueller tube, and by SCINTILLATION COUNTING.

**Radiotherapy Dosage**: The total amount of radiation absorbed by tissues as a result of radiotherapy.

**Protein Engineering**: Procedures by which protein structure and function are changed or created in vitro by altering existing or synthesizing new structural genes that direct the synthesis of proteins with sought-after properties. Such procedures may include the design of MOLECULAR MODELS of proteins using COMPUTER GRAPHICS or other molecular modeling techniques; site-specific mutagenesis (MUTAGENESIS, SITE-SPECIFIC) of existing genes; and DIRECTED MOLECULAR EVOLUTION techniques to create new genes.

**Drug Compounding**: The preparation, mixing, and assembling of a drug. (From Remington, The Science and Practice of Pharmacy, 19th ed, p1814)

**Quality Control**: A system for verifying and maintaining a desired level of quality in a product or process by careful planning, use of proper equipment, continued inspection, and corrective action as required. (Random House Unabridged Dictionary, 2d ed)

**Calibration**: Determination, by measurement or comparison with a standard, of the correct value of each scale reading on a meter or other measuring instrument; or determination of the settings of a control device that correspond to particular values of voltage, current, frequency or other output.

**Data Interpretation, Statistical**: Application of statistical procedures to analyze specific observed or assumed facts from a particular study.

**Equipment Failure Analysis**: The evaluation of incidents involving the loss of function of a device. These evaluations are used for a variety of purposes such as to determine the failure rates, the causes of failures, costs of failures, and the reliability and maintainability of devices.

**Wireless Technology**: Techniques using energy such as radio frequency, infrared light, laser light, visible light, or acoustic energy to transfer information without the use of wires, over both short and long distances.

**Thermodynamics**: A rigorously mathematical analysis of energy relationships (heat, work, temperature, and equilibrium). It describes systems whose states are determined by thermal parameters, such as temperature, in addition to mechanical and electromagnetic parameters. (From Hawley's Condensed Chemical Dictionary, 12th ed)

**Cardiac Resynchronization Therapy**: The restoration of the sequential order of contraction and relaxation of the HEART ATRIA and HEART VENTRICLES by atrio-biventricular pacing.

**Drug Delivery Systems**: Systems for the delivery of drugs to target sites of pharmacological actions. Technologies employed include those concerning drug preparation, route of administration, site targeting, metabolism, and toxicity.

**Tablets**: Solid dosage forms, of varying weight, size, and shape, which may be molded or compressed, and which contain a medicinal substance in pure or diluted form. (Dorland, 28th ed)

**Small Molecule Libraries**: Large collections of small molecules (molecular weight about 600 or less), of similar or diverse nature which are used for high-throughput screening analysis of the gene function, protein interaction, cellular processing, biochemical pathways, or other chemical interactions.

**Artifacts**: Any visible result of a procedure which is caused by the procedure itself and not by the entity being analyzed. Common examples include histological structures introduced by tissue processing, radiographic images of structures that are not naturally present in living tissue, and products of chemical reactions that occur during analysis.

**Excipients**: Usually inert substances added to a prescription in order to provide suitable consistency to the dosage form. These include binders, matrix, base or diluent in pills, tablets, creams, salves, etc.

**Risk Factors**: An aspect of personal behavior or lifestyle, environmental exposure, or inborn or inherited characteristic, which, on the basis of epidemiologic evidence, is known to be associated with a health-related condition considered important to prevent.

**Drug Evaluation, Preclinical**: Preclinical testing of drugs in experimental animals or in vitro for their biological and toxic effects and potential clinical applications.

**Support Vector Machines**: Learning algorithms which are a set of related supervised computer learning methods that analyze data and recognize patterns, and used for classification and regression analysis.

**Alcoholism**: A primary, chronic disease with genetic, psychosocial, and environmental factors influencing its development and manifestations. The disease is often progressive and fatal. It is characterized by impaired control over drinking, preoccupation with the drug alcohol, use of alcohol despite adverse consequences, and distortions in thinking, most notably denial. Each of these symptoms may be continuous or periodic. (Morse & Flavin for the Joint Commission of the National Council on Alcoholism and Drug Dependence and the American Society of Addiction Medicine to Study the Definition and Criteria for the Diagnosis of Alcoholism: in JAMA 1992;268:1012-4)

**Biomass**: Total mass of all the organisms of a given type and/or in a given area. (From Concise Dictionary of Biology, 1990) It includes the yield of vegetative mass produced from any given crop.

**Hydrogen-Ion Concentration**: The normality of a solution with respect to HYDROGEN ions; H+. It is related to acidity measurements in most cases by pH = log 1/2[1/(H+)], where (H+) is the hydrogen ion concentration in gram equivalents per liter of solution. (McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)

**Mental Disorders**: Psychiatric illness or diseases manifested by breakdowns in the adaptational process expressed primarily as abnormalities of thought, feeling, and behavior producing either distress or impairment of function.

**Research Design**: A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly.

**Quantitative Structure-Activity Relationship**: A quantitative prediction of the biological, ecotoxicological or pharmaceutical activity of a molecule. It is based upon structure and activity information gathered from a series of similar compounds.

**Databases, Factual**: Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.

**Neoplasms**: New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms.

**Amino Acid Sequence**: The order of amino acids as they occur in a polypeptide chain. This is referred to as the primary structure of proteins. It is of fundamental importance in determining PROTEIN CONFORMATION.

**Solubility**: The ability of a substance to be dissolved, i.e. to form a solution with another substance. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)

**United States**

**Escherichia coli**: A species of gram-negative, facultatively anaerobic, rod-shaped bacteria (GRAM-NEGATIVE FACULTATIVELY ANAEROBIC RODS) commonly found in the lower part of the intestine of warm-blooded animals. It is usually nonpathogenic, but some strains are known to produce DIARRHEA and pyogenic infections. Pathogenic strains (virotypes) are classified by their specific pathogenic mechanisms such as toxins (ENTEROTOXIGENIC ESCHERICHIA COLI), etc.

**Crystallography, X-Ray**: The study of crystal structure using X-RAY DIFFRACTION techniques. (McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed)

**Biomechanical Phenomena**: The properties, processes, and behavior of biological systems under the action of mechanical forces.

**Bayes Theorem**: A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.

**Nanoparticles**: Nanometer-sized particles that are nanoscale in three dimensions. They include nanocrystaline materials; NANOCAPSULES; METAL NANOPARTICLES; DENDRIMERS, and QUANTUM DOTS. The uses of nanoparticles include DRUG DELIVERY SYSTEMS and cancer targeting and imaging.

**Protein Conformation**: The characteristic 3-dimensional shape of a protein, including the secondary, supersecondary (motifs), tertiary (domains) and quaternary structure of the peptide chain. PROTEIN STRUCTURE, QUATERNARY describes the conformation assumed by multimeric proteins (aggregates of more than one polypeptide chain).

**Batch Cell Culture Techniques**: Methods for cultivation of cells, usually on a large-scale, in a closed system for the purpose of producing cells or cellular products to harvest.

**Radiographic Image Interpretation, Computer-Assisted**: Computer systems or networks designed to provide radiographic interpretive information.

**Particle Size**: Relating to the size of solids.

**Prevalence**: The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time.

**Sequence Analysis, DNA**: A multistage process that includes cloning, physical mapping, subcloning, determination of the DNA SEQUENCE, and information analysis.

**Finite Element Analysis**: A computer based method of simulating or analyzing the behavior of structures or components.

**Radiographic Image Enhancement**: Improvement in the quality of an x-ray image by use of an intensifying screen, tube, or filter and by optimum exposure techniques. Digital processing methods are often employed.

**Computing Methodologies**: Computer-assisted analysis and processing of problems in a particular area.

**Kinetics**: The rate dynamics in chemical or physical systems.

**Normal Distribution**: Continuous frequency distribution of infinite range. Its properties are as follows: 1, continuous, symmetrical distribution with both tails extending to infinity; 2, arithmetic mean, mode, and median identical; and 3, shape completely determined by the mean and standard deviation.

**User-Computer Interface**: The portion of an interactive computer program that issues messages to and receives commands from a user.

**Pharmaceutical Preparations**: Drugs intended for human or veterinary use, presented in their finished dosage form. Included here are materials used in the preparation and/or formulation of the finished dosage form.

**Automation**: Controlled operation of an apparatus, process, or system by mechanical or electronic devices that take the place of human organs of observation, effort, and decision. (From Webster's Collegiate Dictionary, 1993)

**Sequence Analysis, Protein**: A process that includes the determination of AMINO ACID SEQUENCE of a protein (or peptide, oligopeptide or peptide fragment) and the information analysis of the sequence.

**Magnetic Resonance Imaging**: Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques.

**Delayed-Action Preparations**: Dosage forms of a drug that act over a period of time by controlled-release processes or technology.

**Organs at Risk**: Organs which might be damaged during exposure to a toxin or to some form of therapy. It most frequently refers to healthy organs located in the radiation field during radiation therapy.

**Drug Carriers**: Forms to which substances are incorporated to improve the delivery and the effectiveness of drugs. Drug carriers are used in drug-delivery systems such as the controlled-release technology to prolong in vivo drug actions, decrease drug metabolism, and reduce drug toxicity. Carriers are also used in designs to increase the effectiveness of drug delivery to the target sites of pharmacological actions. Liposomes, albumin microspheres, soluble synthetic polymers, DNA complexes, protein-drug conjugates, and carrier erythrocytes among others have been employed as biodegradable drug carriers.

**Sequence Alignment**: The arrangement of two or more amino acid or base sequences from an organism or organisms in such a way as to align areas of the sequences sharing common properties. The degree of relatedness or homology between the sequences is predicted computationally or statistically based on weights assigned to the elements aligned between the sequences. This in turn can serve as a potential indicator of the genetic relatedness between the organisms.

**Stochastic Processes**: Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.

**Electrodes**: Electric conductors through which electric currents enter or leave a medium, whether it be an electrolytic solution, solid, molten mass, gas, or vacuum.

**Models, Anatomic**: Three-dimensional representation to show anatomic structures. Models may be used in place of intact animals or organisms for teaching, practice, and study.

**Biomedical Engineering**: Application of principles and practices of engineering science to biomedical research and health care.

**Polymerase Chain Reaction**: In vitro method for producing large amounts of specific DNA or RNA fragments of defined length and sequence from small amounts of short oligonucleotide flanking sequences (primers). The essential steps include thermal denaturation of the double-stranded target molecules, annealing of the primers to their complementary sequences, and extension of the annealed primers by enzymatic synthesis with DNA polymerase. The reaction is efficient, specific, and extremely sensitive. Uses for the reaction include disease diagnosis, detection of difficult-to-isolate pathogens, mutation analysis, genetic testing, DNA sequencing, and analyzing evolutionary relationships.

**Feasibility Studies**: Studies to determine the advantages or disadvantages, practicability, or capability of accomplishing a projected plan, study, or project.

**Biosensing Techniques**: Any of a variety of procedures which use biomolecular probes to measure the presence or concentration of biological molecules, biological structures, microorganisms, etc., by translating a biochemical interaction at the probe surface into a quantifiable physical signal.

**Chromatography, High Pressure Liquid**: Liquid chromatographic techniques which feature high inlet pressures, high sensitivity, and high speed.

**Codon**: A set of three nucleotides in a protein coding sequence that specifies individual amino acids or a termination signal (CODON, TERMINATOR). Most codons are universal, but some organisms do not produce the transfer RNAs (RNA, TRANSFER) complementary to all codons. These codons are referred to as unassigned codons (CODONS, NONSENSE).

**Genetic Engineering**: Directed modification of the gene complement of a living organism by such techniques as altering the DNA, substituting genetic material by means of a virus, transplanting whole nuclei, transplanting cell hybrids, etc.

**Cross-Sectional Studies**: Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.

**Internet**: A loose confederation of computer communication networks around the world. The networks that make up the Internet are connected through several backbone networks. The Internet grew out of the US Government ARPAnet project and was designed to facilitate information exchange.

**Prospective Studies**: Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group.

**Databases, Protein**: Databases containing information about PROTEINS such as AMINO ACID SEQUENCE; PROTEIN CONFORMATION; and other properties.

**Radiation Protection**

**Peptides**: Members of the class of compounds composed of AMINO ACIDS joined together by peptide bonds between adjacent amino acids into linear, branched or cyclical structures. OLIGOPEPTIDES are composed of approximately 2-12 amino acids. Polypeptides are composed of approximately 13 or more amino acids. PROTEINS are linear polypeptides that are normally synthesized on RIBOSOMES.

**Base Sequence**: The sequence of PURINES and PYRIMIDINES in nucleic acids and polynucleotides. It is also called nucleotide sequence.

**Protein Binding**: The process in which substances, either endogenous or exogenous, bind to proteins, peptides, enzymes, protein precursors, or allied compounds. Specific protein-binding measures are often used as assays in diagnostic assessments.

**Genetic Vectors**: DNA molecules capable of autonomous replication within a host cell and into which other DNA sequences can be inserted and thus amplified. Many are derived from PLASMIDS; BACTERIOPHAGES; or VIRUSES. They are used for transporting foreign genes into recipient cells. Genetic vectors possess a functional replicator site and contain GENETIC MARKERS to facilitate their selective recognition.

**Substance-Related Disorders**: Disorders related to substance abuse.

**Genetic Enhancement**: The use of genetic methodologies to improve functional capacities of an organism rather than to treat disease.

**Therapy, Computer-Assisted**: Computer systems utilized as adjuncts in the treatment of disease.

**Models, Neurological**: Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.

**Ligands**: A molecule that binds to another molecule, used especially to refer to a small molecule that binds specifically to a larger molecule, e.g., an antigen binding to an antibody, a hormone or neurotransmitter binding to a receptor, or a substrate or allosteric effector binding to an enzyme. Ligands are also molecules that donate or accept a pair of electrons to form a coordinate covalent bond with the central metal atom of a coordination complex. (From Dorland, 27th ed)

**Sleep Disorders**: Conditions characterized by disturbances of usual sleep patterns or behaviors. Sleep disorders may be divided into three major categories: DYSSOMNIAS (i.e. disorders characterized by insomnia or hypersomnia), PARASOMNIAS (abnormal sleep behaviors), and sleep disorders secondary to medical or psychiatric disorders. (From Thorpy, Sleep Disorders Medicine, 1994, p187)

**Surface Properties**: Characteristics or attributes of the outer boundaries of objects, including molecules.

**Child Behavior**: Any observable response or action of a child from 24 months through 12 years of age. For neonates or children younger than 24 months, INFANT BEHAVIOR is available.

**Fuzzy Logic**: Approximate, quantitative reasoning that is concerned with the linguistic ambiguity which exists in natural or synthetic language. At its core are variables such as good, bad, and young as well as modifiers such as more, less, and very. These ordinary terms represent fuzzy sets in a particular problem. Fuzzy logic plays a key role in many medical expert systems.

**Likelihood Functions**: Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.

**Brain**: The part of CENTRAL NERVOUS SYSTEM that is contained within the skull (CRANIUM). Arising from the NEURAL TUBE, the embryonic brain is comprised of three major parts including PROSENCEPHALON (the forebrain); MESENCEPHALON (the midbrain); and RHOMBENCEPHALON (the hindbrain). The developed brain consists of CEREBRUM; CEREBELLUM; and other structures in the BRAIN STEM.

Scheduling

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**optimization****problem**, a slack variable is a variable that is added to an inequality constraint to transform it into an ... Boyd, Stephen P.; Vandenberghe, Lieven (2004). Convex**Optimization**(pdf). Cambridge University Press. ISBN 978-0-521-83378-3. ... Slack Variable Tutorial - Solve slack variable**problems**online. ...Constrained Shortest Path and Related

**Problems**. Constrained Network**Optimization**. VDM Verlag Dr. Müller. ISBN 978-3-8364-4633-4 ..."Efficiency of coordinate descent methods on huge-scale

**optimization****problems**" (PDF). SIAM J.**Optimization**. 22 (2): 341-362. doi ... by iteratively solving the single variable**optimization****problems**x i k + 1 = a r g m i n y ∈ R f ( x 1 k + 1 , … , x i − 1 k + ...**optimization****problems**in a loop. In the simplest case of cyclic coordinate descent, one cyclically iterates through the ... The other**problem**is difficulty in parallelism. Since the nature of Coordinate Descent is to cycle through the directions and ...Solution of non-linear, eigenvalue and time-dependent

**problems**. PDE-constrained**optimization****problems**. Partitioning and load ..."Direct trajectory

**optimization**and costate estimation of finite-horizon and infinite-horizon optimal control**problems**using a ... The techniques have been extensively used to solve a wide range of**problems**such as those arising in UAV trajectory generation ... Solving an optimal control**problem**requires the approximation of three types of mathematical objects: the integration in the ... Hesthaven, J. S.; Gottlieb, S.; Gottlieb, D. (2007). Spectral methods for time-dependent**problems**. Cambridge University Press. ...Modeling those choices or decisions as an

**optimization****problem**provides a means to select the best available set of choices or ... The**optimization****problem**reflects the mathematical complexity required to reach feasible and practical pricing solutions. There ... Many**optimization****problems**are formulated as constrained or unconstrained mathematical programs, either linear programs (LP) or ... In some settings, solutions to this**problem**may be provided by heuristic methods; in others, by numerical**optimization**methods ...Degeneracy in

**optimization****problems**. Springer Netherlands. 46-47 (1): 203-233. CiteSeerX 10.1.1.36.7658 . doi:10.1007/ ...... quadratic

**optimization****problem**, that is, the**problem**of optimizing (minimizing or maximizing) a quadratic function of several ... In the case in which Q is positive definite, the**problem**is a special case of the more general field of convex**optimization**. ... Quadratic programming (QP) is the process of solving a special type of mathematical**optimization****problem**-specifically, a ( ... For large**problems**, the system poses some unusual difficulties, most notably that the**problem**is never positive definite (even ...46-47 (Degeneracy in

**optimization****problems**, number 1): 203-233. CiteSeerX 10.1.1.36.7658 . doi:10.1007/BF02096264. ISSN 0254- ... Researchers have extended the criss-cross algorithm for many**optimization**-**problems**, including linear-fractional programming. ... The criss-cross algorithm has been extended to solve more general**problems**than linear programming**problems**. There are variants ... The criss-cross algorithm can solve quadratic programming**problems**and linear complementarity**problems**, even in the setting of ...Facility location (

**optimization****problem**). Facility location (cooperative game). Vetta, A. (2002). "Nash equilibria in ... and Combinatorial**Optimization**. Algorithms and Techniques. Lecture Notes in Computer Science. 3122. p. 183. doi:10.1007/978-3- ..."New criss-cross type algorithms for linear complementarity

**problems**with sufficient matrices" (pdf).**Optimization**Methods and ... The linear complementarity**problem**L C P ( M , q ) {\displaystyle LCP(M,q)} has a unique solution for every vector q {\ ... Hurwitz matrix Linear complementarity**problem**M-matrix Perron-Frobenius theorem Q-matrix Z-matrix (mathematics) Kellogg, R. B ... Murty, Katta G. (January 1972). "On the number of solutions to the complementarity**problem**and spanning properties of ...J.

**Optimization**Theory Appl. 19 (1976), no. 1, 3--16. Existence theory in optimal control**problems**in the underlying ideas. ...**Optimization**of sequences of operations under constraints on the individual operations. (Polish) Podstawy Sterowania 1 (1971), ... Contribution to the time optimal control**problem**. Abh. Deutsch. Akad. Wiss. Berlin Kl. Math. Phys. Tech. 1965 1965 no. 2, 438- ... with Węgrzyn, S. ; Skowronek, M.**Optimization**of a sequence of operations at limitations imposed on particular operations. Bull ...SIAM Journal on

**Optimization**. 19 (4): 1574. doi:10.1137/070704277.**Problem**Complexity and Method Efficiency in**Optimization**, A ... In engineering,**optimization****problems**are often of this type, when you do not have a mathematical model of the system (which ... to solve**optimization****problems**and fixed point equations (including standard linear systems) when the collected data is subject ... To address this**problem**, Spall proposed the use of simultaneous perturbations to estimate the gradient. This method would ...Structural and Multidisciplinary

**Optimization**. 37 (3): 239-253. doi:10.1007/s00158-008-0234-7. Hayya, Jack; Armstrong, Donald; ... "A comparative study of uncertainty propagation methods for black-box-type**problems**". ...... is used to solve non-linear least squares

**problems**. These minimization**problems**arise especially in least squares curve fitting ... Some of them support only basic unconstrained**optimization**whilst other ones support different combinations of box and linear ... The primary application of the Levenberg-Marquardt algorithm is in the least-squares curve fitting**problem**: given a set of m {\ ... The absolute values of any choice depends on how well-scaled the initial**problem**is. Marquardt recommended starting with a ...doi:10.1016/S0167-9473(96)00024-2. "Some Aperture-Angle

**Optimization****Problems**". Algorithmica. 33 (4): 411-435. 2002-08-01. doi: ... Teichmann, Marek (1989). "Wedge placement**optimization****problems**". Godfried T. Toussaint, "A simple linear algorithm for ... Spiral triangulations Quadrangulation Nice triangulation Art gallery**problem**Wedge placement**optimization****problem**Union of two ... the method of rotating calipers is an algorithm design technique that can be used to solve**optimization****problems**including ...2011). "Direct Trajectory

**Optimization**and Costate Estimation of Finite-Horizon and Infinite-Horizon Optimal Control**Problems**... 2011). "Direct Trajectory**Optimization**Using a Variable Low-Order Adaptive Pseudospectral Method". Journal of Spacecraft and ... where the software has been used for minimum-time**optimization**of low-thrust orbital transfers, where the software has been ... GPOPS-II (pronounced "GPOPS 2") is a general-purpose MATLAB software for solving continuous optimal control**problems**using hp- ...The

**optimization****problem**is a Lasso**problem**, and thus it can be efficiently solved with a state-of-the-art Lasso solver such as ... HSIC Lasso**optimization****problem**is given as H S I C L a s s o : min x 1 2 ∑ k , l = 1 n x k x l HSIC ( f k , f l ) − ∑ k = 1 n ... Let xi be the set membership indicator function for feature fi; then the above can be rewritten as an**optimization****problem**: C F ...**Optimization**Letters, November 2011. R. Meiri et J. Zahavi. Using simulated annealing to optimize the feature selection**problem**...To establish whether a form h(x) is SOS amounts to solving a convex

**optimization****problem**. Indeed, any h(x) can be written as h ... This is a linear matrix inequality (LMI) feasibility test, which is a convex**optimization****problem**. The expression h ( x ) = x ... doi:10.1007/s10589-012-9513-8. Sum-of-squares**optimization**Positive polynomial Hilbert's seventeenth**problem**. ... is SOS amounts to solving a convex**optimization****problem**. Indeed, similarly to the scalar case any F(x) can be written according ...A natural approach to this

**problem**would be to compute a finite number of powers of the given graph G, find their independence ... Discrete Mathematics and**Optimization**. 78 (2nd ed.). John Wiley & Sons. p. 134. ISBN 1118640217. Regan, Kenneth W. (July 10, ... However (even ignoring the computational difficulty of computing the independence numbers of these graphs, an NP-hard**problem**) ... Open**Problem**Garden . Alon, Noga; Lubetzky, Eyal (2006), "The Shannon capacity of a graph and the independence numbers of its ...However, sphere packing

**problems**can be generalised to consider unequal spheres, n-dimensional Euclidean space (where the ... For learning about oriented matroids, a good preparation is to study the textbook on linear**optimization**by Nering and Tucker, ... This theorem has many equivalent versions and analogs and has been used in the study of fair division**problems**. Topics in this ... In 1978 the situation was reversed - methods from algebraic topology were used to solve a**problem**in combinatorics - when ...Usually, this

**optimization**is done in order to achieve a low decoding error probability without hurting too much the data rate ... Interleaving ameliorates this**problem**by shuffling source symbols across several code words, thereby creating a more uniform ... IT++: a C++ library of classes and functions for linear algebra, numerical**optimization**, signal processing, communications, and ...In this paper we move a step towards the formalization of this discipline by describing some

**optimization****problems**that arise ... Compact linearization for binary quadratic**problems**- Liberti 11. Reformulation and convex relaxation techniques for global ...**Optimization**by simulated annealing: An experimental evaluation - Johnson, Aragon, et al. - 1989 ... Quadratic Convex Reformulation : A Computational Study of the Graph Bisection**Problem**- Billionnet, Elloumi, et al. - 2006 ...... Mohammed Azmi Al-Betar,1,2 Ahamad Tajudin Khader,1 Mohammed A. Awadallah,1 ... L. Zhang, Y. Xu, and Y. Liu, "An elite decision making harmony search algorithm for

**optimization****problem**," Journal of Applied ... M. Mahdavi, M. Fesanghary, and E. Damangir, "An improved harmony search algorithm for solving**optimization****problems**," Applied ... "**Problem**denitions and evaluation criteria for the CEC 2005 special session on real-parameter**optimization**," Tech. Rep., Nanyang ...An

**optimization****problem**with discrete variables is known as a discrete**optimization**. In a discrete**optimization****problem**, we are ... an**optimization****problem**is the**problem**of finding the best solution from all feasible solutions.**Optimization****problems**can be ... the**problem**is more naturally characterized as an**optimization****problem**. An NP-**optimization****problem**(NPO) is a combinatorial ... Semi-infinite programming Search**problem**Counting**problem**(complexity) Function**problem**Glove**problem**Operations research. ...... for solving binary

**optimization****problems**by using a new solution update rule because the agents in AAA work on continuous... ...**problems**which are pure binary**optimization****problem**and there is no integer or real valued decision variables in this**problem**. ... and this modification for AAA is required for solving a binary**optimization****problem**because a binary**optimization****problems**have ... Yuan XH et al (2009) An improved binary particle swarm**optimization**for unit commitment**problem**. Expert Syst Appl 36(4):8049- ...... Finn Kydland. Chapter in NBER book Annals of ...

The algorithmic framework ARGONAUT is presented for the global

**optimization**of general constrained grey-box**problems**. ARGONAUT ... ARGONAUT is tested on a large set of test**problems**for constrained global**optimization**with a large number of input variables ... Martelli, E., Amaldi, E.: PGS-COM: a hybrid method for constrained non-smooth black-box**optimization****problems**: brief review, ... Boukouvala, F., Hasan, M.M.F., Christodoulos, A.F.: Global**optimization**of constrained grey-box**problems**: new method and ...**Optimization**in Solving Elliptic

**Problems**focuses on one of the most interesting and challenging

**problems**of computational ...

**Optimization**in Solving Elliptic

**Problems**focuses on one of the most interesting and challenging

**problems**of computational ...

**Optimization**in Solving Elliptic

**Problems**describes the construction of computational algorithms resulting in the required ... In addition, algorithms are developed for eigenvalue

**problems**and Navier-Stokes

**problems**. The development of these algorithms ...

The primary aim of Mathematical

**Problems**in Engineering is rapid publication and dissemination of important mathematical work ... Mathematical**Problems**in Engineering is a peer-reviewed, Open Access journal that publishes results of rigorous engineering ... An unconstrained global**optimization**(UGO)**problem**can generally be formulated as follows: where is an objective function and ... C. C. Chen, "Two-layer particle swarm**optimization**for unconstrained**optimization****problems**," Applied Soft Computing Journal, ...... the smoothed penalty

**problem**and the original**optimization****problem**are obtained. Based on the smoothed**problem**, an algorithm ... Error estimations among the optimal objective values of the nonsmooth penalty**problem**, ... a second-order differentiability smoothing technique to the classical l 1 exact penalty function for constrained**optimization**... the smoothed penalty**problem**and the original**optimization****problem**are obtained. Based on the smoothed**problem**, an algorithm ...Obtained results for the

**problems**show that iABC is better than the basic ABC in terms of solution quality. ... method called as iABC were applied to both five numerical benchmark functions and an estimation of energy demand**problem**. ... algorithm is a swarm-based metaheuristic**optimization**technique, developed by inspiring foraging and dance behaviors of honey ... Kiran, M. and Babalik, A. (2014) Improved Artificial Bee Colony Algorithm for Continuous**Optimization****Problems**. Journal of ...The biological inspired

**optimization**techniques have proven to be powerful tools for solving scheduling**problems**. Marriage in ... Marriage in Honeybee**Optimization**to Scheduling**Problems**: 10.4018/978-1-61350-086-6.ch008: ... "Marriage in Honeybee**Optimization**to Scheduling**Problems**." Hybrid Algorithms for Service, Computing and Manufacturing Systems: ... "Marriage in Honeybee**Optimization**to Scheduling**Problems**." In Hybrid Algorithms for Service, Computing and Manufacturing ...The inverse

**problem**is then posed as an**optimization****problem**, where the function to be optimized is variously called the ... A separate section is devoted to a subject that is not encountered in all**optimization****problems**but is particularly important ... Next the connection with**optimization**methods is made by presenting a general formulation of geophysical inverse**problems**. This ... The interdisciplinary area between inverse**problems**in geophysics and**optimization**methods in mathematics was specifically ......

**optimization****problem**arises as a side constraint. One of the motivating factors was the concept of the Stackelberg solution in ... engineers and economists started to pay c10se attention to the**optimization****problems**in which another (lower-Ievel) ... Nonsmooth Approach to**Optimization****Problems**with Equilibrium Constraints. Theory, Applications and Numerical Results. Authors: ... Nonsmooth Approach to**Optimization****Problems**with Equilibrium Constraints. Book Subtitle. Theory, Applications and Numerical ...**Optimization**Models and Mathematical Solutions. Authors. * Julia Kallrath Series Title. Applied

**Optimization**. Series Volume. 91 ... Online Storage Systems and Transportation

**Problems**with Applications.

**Optimization**Models and Mathematical Solutions. Authors: ... The second

**problem**originates in the health sector and leads to a vehicle routing

**problem**. Reasonable solutions for the offline ... It is shown that this logistic

**problem**leads to an NP-hard Batch PreSorting

**problem**which is not easy to solve optimally in ...

Approaches for performing simulation

**optimization**for solving a constrained**optimization****problem**are generally disclosed. One ... data processing**problems**, network flow**problems**, and optimal control**problems**. A constrained**optimization****problem**generally ...**Optimization****problems**associated with constraints are referred to as constrained**optimization****problems**. Some examples include ... Conventionally, to solve a constrained**optimization****problem**, the**problem**is first represented in a mathematical formulation, ...... either ending at an optimal solution of the equivalent

**problem**with a complementarity constraint, or converging to an optimal ... which are successfully applied in global**optimization**. Some illustrative examples and results on computational experiments are ... we propose a method for finding the global optimum of a class of nonlinear bilevel programming**problems**. The main idea of this ... Global**optimization**algorithm for solving bilevel programming**problems**with quadratic lower levels. Paul B. Hermanns 1, and ...Real world

**problems**in engineering domain are generally large scale or nonlinear or constrained**optimization****problems**. Since ... Numerical results of the structural design**optimization****problems**are reported and compared. As shown, the solutions by the ... Also we can say, our results indicate that the proposed approach may yield better solutions to engineering**problems**than those ... algorithm is presented to search the optimal solution of the**problem**in the feasible region of the entire search space. ...4) The

**problem**of overfitting. 5) Regularization for linear models. Do you have technical**problems**? Write to us: [email protected] ... And well start with the discussion of overfitting**problem**. So suppose that we have a classification**problem**. And weve just ... Overfitting**problem**and model validation. To view this video please enable JavaScript, and consider upgrading to a web browser ... There are some**problems**with this approach with holdout set. For example, if the sample is small, we want to see what happens ...Dynamic

**optimization****problems**can be numerically solved by direct, indirect and Hamilton-Jacobi-Bellman methods. In this paper ... The proposed hybrid method is illustrated with a pressure-constrained batch reactor**optimization****problem**associated with the ... A novel hybrid**optimization**algorithm for diferential-algebraic control**problems**. Braz. J. Chem. Eng. [online]. 2007, vol.24, n ... transformation of the original**problem**into a standard nonlinear programming**problem**that provides control and state variables ...Chaotic Tornadogenesis

**Optimization**Algorithm for Data Clustering**Problems**: 10.4018/IJSSCI.2018010104: This article describes ... "Chaotic Tornadogenesis**Optimization**Algorithm for Data Clustering**Problems**," International Journal of Software Science and ... Recently a new**optimization**algorithm TOA was developed to address these**problems**. However, the standard TOA is too often ... "Chaotic Tornadogenesis**Optimization**Algorithm for Data Clustering**Problems**." IJSSCI 10.1 (2018): 38-64. Web. 21 Feb. 2018. doi: ...Applying Ant Colony

**Optimization**algorithms to solve the Traveling Salesman**Problem**.; Author: geoyar; Updated: 13 Sep 2013; ... The Traveling Salesman**Problem**. The quote from the "Ant Colony**Optimization**": The Traveling Salesman**Problem**is a**problem**of a ... I was always interested in Artificial Intelligence**problems**. So when I saw the article "Genetic and Ant Colony**Optimization**... Ant Colony**Optimization**to solve a classic Asymmetric Travelling Sales Man**Problem**...A particular focus will be given to

**optimization****problems**that arise in the application areas telecommunication, traffic and ... Several algorithms for the above mentioned fundmental combinatorial**optimization****problems**were developed and analyzed within ... analyze and experimentally evaluate algorithms for fundamental combinatorial**optimization****problems**. ... Approximating connected facility location**problems**via random facility sampling and core detouring. In Proceedings of the 19th ...... processing several

**optimization**iterations simultaneously, reducing multidimensional**optimization****problems**using multiple Peano ... we describe the Globalizer software system for solving the global**optimization****problems**. The system is designed to maximize the ... capabilities with shared and distributed memory and with large numbers of processors to solve the global**optimization****problems**... potential of the modern high-performance computational systems in order to solve the most time-consuming**optimization****problems**...The aim of the book is to cover the three fundamental aspects of research in equilibrium

**problems**: the statement**problem**and ... Nonsmooth**Optimization**and Variational Inequality Models ebook online in PDF format for iPhone, iPad, Android, Computer and ... On the existence of solutions to vector**optimization****problems**; G. Mastroeni, M. Pappalardo. Equilibrium**problems**and ... Equilibrium**Problems**: Nonsmooth**Optimization**and Variational Inequality Models. by F. Giannessi(ed.) ; A. Maugeri(ed.) ; Panos ...... algorithm to a flow shop scheduling

**problem**. Permutation encoding of job indices ... This paper presents a method of applying particle swarm**optimization**(PSO) ...SolveCombinatorial optimization problemsNonsmooth OptimizationNonlinear optimizationAlgorithm for constrained optimization problemsColony optimizationParticleComputational Intelligence In Expensive Optimization ProblemsConvex optimizationStochastic optimization20162017IntegerExpensive optimization problemsObjectiveOptimalMultiobjectiveArtificial Bee ColonConstraintAbstractHeuristicConstraintsQuadraticShape optimizationSpringerEvolutionary ComputationAlgebraicJournal of Global OptimizationDerivative-free optimizationGeneticMinimizationComplex optimization problemsDesign optimization problemsMathematical ProgrammingFeasible solutionsSolvingGlobal optimization algorithmNonconvexParametersPrinceton UniveTopology optimizationReal-parameter optimizationRobustnessPaper proposesSimulationUncertainty

- The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. (hindawi.com)
- Many conventional nonlinear programming (NLP) techniques, such as the golden search, quadratic approximation, Nelder-Mead, steepest descent, Newton, and conjugate gradient methods, have been used to solve UGO problems [ 1 ]. (hindawi.com)
- Furthermore, Chen [ 3 ] presented a two-layer PSO method to solve nine UGO problems. (hindawi.com)
- This books covers the analysis and development of online algorithms involving exact optimization and heuristic techniques, and their application to solve two real life problems. (springer.com)
- It is shown that this logistic problem leads to an NP-hard Batch PreSorting problem which is not easy to solve optimally in offline situations. (springer.com)
- The branch-and-bound method developed is suitable to solve any kind of sequencing-scheduling problem involving accumulative objective functions and constraints, which can be evaluated sequentially. (springer.com)
- The column enumeration approach the author has developed to solve this hospital problem is of general nature and thus can be embedded into any decision-support system involving assigning, sequencing and scheduling. (springer.com)
- The main goal of the present paper is to solve structural engineering design optimization problems with nonlinear resource constraints. (aimsciences.org)
- A. H. Gandomi, X. S. Yang and A. Alavi, Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems,, Engineering with Computers , 29 (2003), 17. (aimsciences.org)
- The system is designed to maximize the use of computational potential of the modern high-performance computational systems in order to solve the most time-consuming optimization problems. (aimsciences.org)
- These novelties provide for the use of the supercomputer system capabilities with shared and distributed memory and with large numbers of processors to solve the global optimization problems efficiently. (aimsciences.org)
- In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. (psu.edu)
- to solve constrained multi-objective problems eciently. (psu.edu)
- The aim of this work is to show the use of a well-known type of evolutionary computation optimization technique, the Ant Colony Optimization (ACO), in order to solve different electromagnetic problems: array synthesis both linear and planar and with different design criteria, design of a monopolar Ultra Wide Band (UWB) microstrip antenna and reduction of E-plane mutual coupling in a multilayer patch antennas array. (cst.com)
- We then discuss its equivalence with a specialized column generation scheme and how its framework can be generalized to solve convex optimization problems. (gerad.ca)
- This paper describes a noise-aware dominance operator for evolutionary algorithms to solve the multiobjective optimization problems (MOPs) that contain noise in their objective functions. (sciweavers.org)
- In order to solve these problems exact, we developed a Branch & Bound routine. (logos-verlag.de)
- Exact algorithms are impractical since they fail to solve this problem for multiple instances of long lengths in polynomial time. (springer.com)
- There are some approximations, heuristic, and metaheuristic methods proposed to solve the problem. (springer.com)
- In this paper, we have proposed chemical reaction optimization technique to solve the longest common subsequence problem for multiple instances. (springer.com)
- The accuracy of the ED solution depends on various factors matical approaches such as Least Error Square (LES) and Gauss such as the selected model, the data and parameters used, as well Newton have been used to solve estimation problems in power as the optimization tool used for solving the problem. (scribd.com)
- Optimization approach to solve this problem is presented and a posteriori error estimates in the Tikhonov functional and Lagrangian are formulated. (arxiv.org)
- Then combined the adjustment model with migrating birds optimization algorithm to solve standard benchmark problems and proved its validity. (atlantis-press.com)
- Xu, Ji 2016-10-18 00:00:00 Human beings solve problems in different granularity worlds and shift from one granularity world to another quickly. (deepdyve.com)
- it is more convenient to solve minimization problems. (wikipedia.org)
- Quickly solve complex optimization problems. (sas.com)
- Mike Gilliland, Product Marketing Manager at SAS, demonstrates how you can use SAS Optimization to build and solve an optimization model that guides financial investment decisions. (sas.com)
- Enables you to produce a range of models, including linear, mixed integer linear, nonlinear, quadratic and network optimization, as well as solve constraint satisfaction problems. (sas.com)
- Aggressive presolvers reduce effective problem size so you can tackle large problems and solve them more quickly. (sas.com)
- Recently, there has been much interest in the possibility of using adiabatic quantum optimization (AQO) to solve NP-complete and NP-hard problems [ 1 , 2 ] 1 . (frontiersin.org)
- To solve the optimization problem, we need to count the number of linear extensions of the partial order, which is known to be #P-complete 4 . (nature.com)
- Moreover, a structured greedy algorithm is proposed to efficiently solve the structured sparsity problem. (psu.edu)
- This short story, as stupid and idiotic as it may seem corresponds to an engineering problem and hopefully by the end of this post, we will solve it. (blogspot.ca)
- Now how do we solve this constrained optimization problem ? (blogspot.ca)
- Using a faster view layer like React for specific views would help solve this problem. (medium.com)

- The aim of this project is to design, analyze and experimentally evaluate algorithms for fundamental combinatorial optimization problems. (tu-berlin.de)
- Several algorithms for the above mentioned fundmental combinatorial optimization problems were developed and analyzed within the project. (tu-berlin.de)
- Combinatorial optimization problems such as routing, scheduling, covering and packing problems abound in everyday life. (umd.edu)
- Unfortunately, a large number of natural and interesting combinatorial optimization problems are NP-hard. (umd.edu)
- Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. (nottingham.ac.uk)
- The combinatorial optimization problems such as Travelling Salesman Problem, Minimum Spanning Tree Problem, Vehicle Routing Problem etc. aims at finding an optimal object from a finite set of objects. (bartleby.com)

- Four others for some download equilibrium problems: nonsmooth optimization and variational inequality models. (nortekmechanical.ca)
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- De som k pt den h r boken har ofta ocks k pt Nonsmooth Optimization and Related Topics av F H Clarke, Vladimir F Dem'Yanov, F Giannessi (h ftad). (bokus.com)

- Multistart algorithm for nonconvex nonlinear optimization. (sas.com)
- LSO solver can be used with (generally nonlinear) optimization problems that don't adhere to the assumptions that conventional optimization solvers make. (sas.com)
- ALGLIB - dual licensed (GPL/commercial) constrained quadratic and nonlinear optimization library with C++ and C# interfaces. (wikipedia.org)
- AMPL - modelling language for large-scale linear, mixed integer and nonlinear optimization. (wikipedia.org)
- Artelys Knitro - large scale nonlinear optimization for continuous and mixed-integer programming. (wikipedia.org)
- IOSO - (Indirect Optimization on the basis of Self-Organization) a multiobjective, multidimensional nonlinear optimization technology. (wikipedia.org)
- Excel add-in performs linear, integer, and nonlinear optimization using LINDO. (wikipedia.org)

- Many stochastic global optimization (SGO) approaches developed to overcome this limitation of the traditional NLP methods include genetic algorithms (GAs), particle swarm optimization (PSO), ant colony optimization (ACO), and artificial immune algorithms (AIAs). (hindawi.com)
- Dorigo, M. and Stützle, T. (2004) Ant Colony Optimization. (scirp.org)
- So when I saw the article "Genetic and Ant Colony Optimization Algorithms" by Peter Kohout, I immediately downloaded it. (codeproject.com)
- The AI aficionados will find there possibilities to play with algorithms of Artificial Ant Colony Optimization and their parameters. (codeproject.com)
- Definitions of Ant Colony Optimization algorithms can be found in the book Ant Colony Optimization by Marco Dorigo and Thomas Stützle. (codeproject.com)
- Also very useful was Ant Colony Optimization for Tree and Hypertree Decompositions , Master Thesis by Thomas Hammerl of Vienna. (codeproject.com)
- I used it as a reference to Ant Colony Optimization algorithms. (codeproject.com)
- Our approach is compared with hyper-heuristic, ant colony optimization, beam ant colony optimization, and memory-bound anytime algorithms. (springer.com)

- The results obtained by binAAA are compared with the results of state-of-art algorithms such as artificial bee colony, particle swarm optimization, and genetic algorithms. (springer.com)
- Shang L, Zhou Z, Liu X (2016) Particle swarm optimization-based feature selection in sentiment classification. (springer.com)
- Taormina R, Chau KW (2015) Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and extreme learning machines. (springer.com)
- Stochastic global optimization (SGO) algorithms such as the particle swarm optimization (PSO) approach have become popular for solving unconstrained global optimization (UGO) problems. (hindawi.com)
- L. C. Cagnina, S. C. Esquivel and C. A. C. Coello, Solving engineering optimization problems with the simple constrained particle swarm optimizer,, Informatica , 32 (2008), 319. (aimsciences.org)
- L. S. Coelho, Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems,, Expert Systems with Applications , 37 (2010), 1676. (aimsciences.org)
- Q. He and L. Wang, An effective co - evolutionary particle swarm optimization for constrained engineering design problems,, Engineering Applications of Artificial Intelligence , 20 (2007), 89. (aimsciences.org)
- T. Cura , Particle swarm optimization approach to portfolio optimization, Nonlinear Analysis: Real World Applications , 10 (2009), 2396-2406. (aimsciences.org)
- R. Karthi, Rajendran, Cb, and K. Ramesh Kumar, "Neighborhood search assisted particle swarm optimization (NPSO) algorithm for partitional data clustering problems", Communications in Computer and Information Science, vol. 192 CCIS, pp. 552-561, 2011. (amrita.edu)
- The proposed method includes a novel multi-objective hybrid approach called MHPV, a hybrid of two known multi-objective algorithms: namely, multi-objective particle swarm optimization (MOPSO) and adapted multi-objective variable neighborhood search (AMOVNS). (repec.org)
- Y. Hou, L. Lu, X. Xiong, and Y. Wu, "Economic Dispatch of Power Systems Based on the Modified Particle Swarm Optimization Algorithm," IEEE/PES Transmission and Distribution Conference and Exhibition, pp. 1-6, doi:10.1109/ TDC.2005.1546751, Aug. 2005. (koreascience.or.kr)
- A particle in PSO is analogous to a bird or fish flying through a search (problem) area. (bartleby.com)
- E. Particle Swarm Technique Particle swarm optimization (PSO) is initialized with a group of random particles (solutions) and then searches for optima by updating generations. (bartleby.com)
- A memetic particle swarm optimization algorithm for multimodal optimization problems. (dmu.ac.uk)
- Wang, H., Moon, I.-K. and Yang, S. (2012) A memetic particle swarm optimization algorithm for multimodal optimization problems. (dmu.ac.uk)
- In this paper, a memetic algorithm that hybridizes particle swarm optimization (PSO) with a local search (LS) technique, called memetic PSO (MPSO), is proposed for locating multiple global and local optimal solutions in the fitness landscape of MMOPs. (dmu.ac.uk)

- Deep Time has the surplus in a private download Computational Intelligence in Expensive Optimization Problems at growth and test. (patentstation.com)
- be the download Computational Intelligence in Expensive Optimization Problems request to design costs. (patentstation.com)

- A. Ben-Tal and A. Nemirovski , Selected topics in robust convex optimization, Mathematical Programming , 112 (2008), 125-158. (aimsciences.org)

- The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. (coursera.org)
- Linear models are basic building blocks for many deep architectures, and stochastic optimization is used to learn every model that we'll discuss in our course. (coursera.org)
- Two basic problems in finite stochastic optimization. (wordpress.com)
- Furthermore, RG 4 and RG 8 investigate aspects of uncertainty in energy management and transport via stochastic optimization or uncertainty quantification, respectively. (wias-berlin.de)

- Mirjalili S, Lewis A (2016) The Whale optimization algorithm. (springer.com)
- Caraveo C, Valdez F, Castillo O (2016) Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation. (springer.com)
- C. A. Floudas and M. P. Pardalos, Recent Advances in Global Optimization , Princeton University Press, 2016. (aimsciences.org)
- Xiangru Lian, Huan Zhang, Cho-Jui Hsieh, Yijun Huang, and Ji Liu , " A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order ", NIPS, 2016. (rochester.edu)

- Investigations and developments are made in recent decades to find optimal solutions for large and dynamic problems using nature-inspired algorithms (Chakraborty, Amrita, & Kumar Kar, 2017). (igi-global.com)
- Many researchers have developed numerous optimization algorithms by looking into the nature, looking into the biology and tried to model some of the impressive and intellectual mechanisms (Pedrycz, & Witold, 2010) into new algorithms for different engineering applications (Bozorg-Haddad, & Omid, 2017). (igi-global.com)
- Blum C, Blesa MJ (2017) A hybrid evolutionary algorithm based on solution merging for the longest arc-preserving common subsequence problem. (springer.com)
- Jie Zhong, Yijun Huang, and Ji Liu , " Asynchronous Parallel Empirical Variance Guided Algorithms for the Thresholding Bandit Problem ", 2017. (rochester.edu)
- Our Stable Bayesian Optimization work won " Best Student Paper Award " at the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Springer 2017. (google.com)

- The performance of the proposed algorithm, binAAA for short, is investigated on the uncapacitated facility location problems which are pure binary optimization problem and there is no integer or real valued decision variables in this problem. (springer.com)
- In a discrete optimization problem, we are looking for an object such as an integer, permutation or graph from a finite (or possibly countably infinite) set. (wikipedia.org)
- Nonconvex quadratic reformulations and solvable conditions for mixed integer quadratic programming problems. (aimsciences.org)
- In Proceedings of the 13th Conference on Integer Programming and Combinatorial Optimization , 2008, to appear. (tu-berlin.de)
- Such problems include mixed-integer optimization, optimization under uncertainty, derivative-free optimization, multilevel optimization, complementarity problems, and optimization applied to game theoretic models. (anl.gov)
- We cast the partial order inference problem as a rational linear integer program, which allows us to present detailed analytical results on the achievable limits in terms of the tradeoff between expected precision and partial order density. (nature.com)
- APMonitor - modeling language and optimization suite for large-scale, nonlinear, mixed integer, differential and algebraic equations with interfaces to MATLAB, Python, and Julia. (wikipedia.org)
- BARON - optimization of algebraic nonlinear and mixed-integer nonlinear problems. (wikipedia.org)
- Mathematica - large-scale multivariate constrained and unconstrained, linear and nonlinear, continuous and integer optimization. (wikipedia.org)

- We consider expensive optimization problems, that is to say problems where each evaluation of the objective function is expensive in terms of computing time, consumption of resources, or cost. (logos-verlag.de)

- Error estimations among the optimal objective values of the nonsmooth penalty problem, the smoothed penalty problem and the original optimization problem are obtained. (repec.org)
- A maximization problem can be treated by negating the objective function. (wikipedia.org)
- Our book is oriented similarly, but we focus on those MPECs which can be treated by the implicit programming approach: the equilibrium constraint locally defines a certain implicit function and allows to convert the problem into a mathematical program with a nonsmooth objective. (springer.com)
- One embodiment according to the present disclosure is to formulate a Lagrange equation having incorporated a Lagrange parameter, a first long run average function for an objective associated with the constrained optimization problem, and a second long run average function for a constraint associated with the constrained optimization problem. (freepatentsonline.com)
- Let Zmax and Zmin be respectively the maximum and minimum of the objective function in a combinatorial problem for which the cardinality of the set of feasible solutions is m and the size of every feasible solution is N. We prove that in a certain probabilistic framework Zmax ~ Zmin almost surely (a.s.) provided log m = o (N) for N and m become large. (inria.fr)
- The contribution of this paper is the development of the PPO problem with the help of the robust optimization approach and the multi-objective IWO algorithm. (aimsciences.org)
- L. Cruz, E. Fernandez, C. Gomez, G. Rivera and F. Perez, Many-objective portfolio optimization of interdependent projects with'a priori'incorporation of decision-maker preferences, Applied Mathematics & Information Sciences , 8 (2014), 1517-1526. (aimsciences.org)
- We illustrate this generalization with a resource constrained nonlinear objective routing problem. (gerad.ca)
- This paper proposes a multi-objective optimization model by integrating sustainability in decision-making, on distribution in a perishable food supply chain network (SCN). (repec.org)
- For the maximization problem, the fitness is simply equal to the value of objective function. (slideserve.com)
- For the minimization problem, the fitness is the reciprocal of the value of objective function. (slideserve.com)
- The FMRSM could be extended to other real-time multi-objective non-linear optimization problems. (iwaponline.com)
- More generally, optimization includes finding "best available" values of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains. (wikipedia.org)
- viz what is the optimum solution for a problem using certain mix of resources that we have, and bearing in mind our objective(s) In Life we seldom find that there is only one solution to a problem, but in fact many possible choices for a solution. (fu-lu-shou.net)
- Excuse my drawing skills below, I need to improve my skills in computer drawing softwares (awaiting your suggestions in the comments section), from it we can write the objective function (or fitness function) to our problem. (blogspot.ca)
- in environmental, economical and energy planning problems Mohammad Asim Nomani PhD Student Department of Statistics & Operations Research Aligarh Muslim University, Aligarh, India Mob: +91-9528072689 Email: [email protected] Multi-objective optimization in environmental and energy planning Energy policy, environmental planning and economic development play a key role in sustainable development. (bartleby.com)
- Kimeme - an open platform for multi-objective optimization and multidisciplinary design optimization. (wikipedia.org)
- modeFRONTIER - an integration platform for multi-objective and multi-disciplinary optimization, which provides a seamless coupling with third party engineering tools, enables the automation of the design simulation process, and facilitates analytic decision making. (wikipedia.org)

- It presents detailed discussions of how asymptotically optimal algorithms may be applied to elliptic problems to obtain numerical solutions meeting certain specified requirements. (routledge.com)
- Construction of asymptotically optimal algorithms is demonstrated for multi-dimensional elliptic boundary value problems under general conditions. (routledge.com)
- Asymptotically Optimal Algorithms for Fourth-Order Elliptic Problems 10. (routledge.com)
- In the field of approximation algorithms, algorithms are designed to find near-optimal solutions to hard problems. (wikipedia.org)
- A problem is additionally called a P-optimization (PO) problem, if there exists an algorithm which finds optimal solutions in polynomial time. (wikipedia.org)
- NPO(III): :The class of NPO problems that have polynomial-time algorithms which computes solutions with a cost at most c times the optimal cost (for minimization problems) or a cost at least 1 / c {\displaystyle 1/c} of the optimal cost (for maximization problems). (wikipedia.org)
- Optimal solutions for typical online instances are computed by an efficient column enumeration approach leading to a set partitioning problem and a set of routing-scheduling subproblems. (springer.com)
- The main idea of this method is to construct iteratively a sequence of points either ending at an optimal solution of the equivalent problem with a complementarity constraint, or converging to an optimal solution. (aimsciences.org)
- Hence, in this article, a penalty guided artificial bee colony (ABC) algorithm is presented to search the optimal solution of the problem in the feasible region of the entire search space. (aimsciences.org)
- In this paper, the differential-algebraic approach is incorporated into a hybrid method, extending the concepts of structural and differential indexes, consistent initialization analysis, index reduction and dynamic degrees of freedom to the optimal control problem. (scielo.br)
- The challenge of optimal clustering lies in finding the optimal number of clusters and identifying all the data groups correctly which is a NP-hard problem. (igi-global.com)
- This result implies that for such a class of combinatorial optimization probblems almost every algorithm finds asymptotically optimal solution. (inria.fr)
- An algorithm for a given optimization problem is said to be exact if it always returns an optimal solution and is said to be efficient if it runs in time polynomial on the size of its input. (umd.edu)
- Moreover, in solving multiobjective problems , designers may be interested in a set of Pareto- optimal points, instead of a single point. (psu.edu)
- G. Buttazzo and L. De Pascale, Optimal Shapes and Masses, and Optimal Transportation Problems, in Optimal Transportation and Applications (Martina Franca, 2001). (esaim-cocv.org)
- G. Buttazzo and E. Stepanov, Optimal Transportation Networks as Free Dirichlet Regions for the Monge-Kantorovich Problem. (esaim-cocv.org)
- Biologically, this minimization problem is motivated by the question of determining the optimal spatial arrangement of favorable and unfavorable regions for a species to survive. (claremont.edu)
- Since their introduction, bio-inspired algorithms, especially the ones based on the social behaviour of the animals that live in colonies have demonstrated great potential in finding near-optimal solutions for both unconstrained and constrained hard optimization problems. (ici.ro)
- There is no polynomial-time algorithm that can be obtain the optimal solution for economic load dispatch problem with non-convex fuel cost functions. (koreascience.or.kr)
- This paper consequently proves that the optimal solution to economic load dispatch problem with non-convex fuel cost functions converges to the valve-point power of each generator. (koreascience.or.kr)
- Optimal solutions to complex business and planning problems. (sas.com)
- Find optimal solutions to difficult problems faster than ever. (sas.com)
- Recently, multimodal optimization problems (MMOPs) have gained a lot of attention from the evolutionary algorithm (EA) community since many real-world applications are MMOPs and may require EAs to present multiple optimal solutions. (dmu.ac.uk)
- Two-stage synthesis problems simultaneously consider here-and-now decisions (e.g., optimal investment) and wait-and-see decisions (e.g., optimal operation). (frontiersin.org)
- Dual properties of sequential gradient Restoration algorithms for optimal control problems. (bokus.com)

- Penalty method-based equilibrium point approach for solving the linear bilevel multiobjective programming problem. (aimsciences.org)

- Kiran, M. and Babalik, A. (2014) Improved Artificial Bee Colony Algorithm for Continuous Optimization Problems. (scirp.org)
- Akay, B. (2009) Performance Analysis of Artificial Bee Colony Algorithm on Numerical Optimization Problems. (scirp.org)
- Singh, A. (2009) An Artificial Bee Colony Algorithm for the Leaf-Constrained Minimum Spanning Tree Problem. (scirp.org)
- Rao, R.S., Narasimham, S. and Ramalingaraju, M. (2008) Optimization of Distribution Network Configuration for Loss Reduction Using Artificial Bee Colony Algorithm. (scirp.org)
- Akay, B. and Karaboga, D. (2012) A Modified Artificial Bee Colony Algorithm for Real-Parameter Optimization. (scirp.org)
- B. Akay and D. Karaboga, Artificial bee colony algorithm for large-scale problems and engineering design optimization,, Journal of Intelligent Manufacturing , 23 (2012), 1001. (aimsciences.org)
- This research is the continuation of our manuscript "Parallelized Multiple Swarm Artificial Bee Colony Algorithm (MS-ABC) for Global Optimization" published in Studies in Informatics and Control, vol. 23(1), 2014 . (ici.ro)

- In the early fifties, applied mathematicians, engineers and economists started to pay c10se attention to the optimization problems in which another (lower-Ievel) optimization problem arises as a side constraint. (springer.com)
- If the capacity constraint on additional storage is neglected the problem has a totally unimodular polyhedron. (springer.com)
- The proposed algorithm has been tested based on a set of well-known constraint benchmark functions and five real-world engineering design problems. (ici.ro)
- You only need to learn one set of statements and commands to build a range of optimization and constraint satisfaction models. (sas.com)
- Solves constraint satisfaction problems using domain reduction/constraint propagation and a choice of search strategies, such as look ahead and backtracking. (sas.com)

- Nature-Inspired Metaheuristic Optimization Algorithms-A Review Pragati Loomba Sonali Tiwari And Neerja Negi Student, Faculty of Computer Applications Assistant Professor, Faculty of Computer Applications Manav Rachna International University Manav Rachna International University Faridabad Faridabad [email protected] [email protected] [email protected] Abstract - Now a day nature-inspired algorithms become a current trend and is applicable to almost every area. (bartleby.com)
- Abstract: Load frequency control problem is considered as one of the most important issues in the design & operation of power systems. (bartleby.com)
- Renewable Energy and Sustainable Development: an Overview PrakashS.V.'* Abstract The Environmental problems that we face today require long-term potential actions for sustainable development. (bartleby.com)

- A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). (nottingham.ac.uk)
- Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. (nottingham.ac.uk)

- ARGONAUT is tested on a large set of test problems for constrained global optimization with a large number of input variables and constraints. (springer.com)
- Later, applications to mechanics and network design have lead to an extension of the problem formulation: Constraints in form of variation al inequalities and complementarity problems were also admitted. (springer.com)
- The term "generalized bi level programming problems" was used at first but later, probably in Harker and Pang, 1988, a different terminology was introduced: Mathematical programs with equilibrium constraints, or simply, MPECs. (springer.com)
- We study the semidefinite programming problem (SDP), i.e the problem of optimization of a linear function of a symmetric matrix subject to linear equality constraints and the additional condition that the matrix be positive semidefinite. (psu.edu)
- We tested our algorithm and present results for both expensive problems with only box constraints and expensive problems with general convex constraints. (logos-verlag.de)
- We formulate the minimization of the power consumption for the MBSs and FBSs as well as an optimization problem to maximize the energy efficiency subject to throughput outage constraints, which can be solved by the Karush-Kuhn-Tucker (KKT) conditions according to the femto tier BS density. (intechopen.com)

- Quadratic Convex Reformulation : A Computational Study of the Graph Bisection Problem - Billionnet, Elloumi, et al. (psu.edu)
- Sundar, S. and Singh, A. (2010) A Swarm Intelligence Approach to the Quadratic Minimum Spanning Tree Problem. (scirp.org)
- Second order optimality conditions and reformulations for nonconvex quadratically constrained quadratic programming problems. (aimsciences.org)
- The quadratic assignment problem, the location problem on graphs, and some pattern matching problems fall into this class. (inria.fr)
- L. S. Coelho and V. C. Mariani, "Combining of Chaotic Differential Evolution and Quadratic Programming for Economic Dispatch Optimization with Valve-Point Effect," IEEE Trans. (koreascience.or.kr)

- A. Fasano, Some free boundary problems with industrial applications, in Shape optimization and free boundaries (Montreal, PQ, 1990) , NATO Adv. Sci. (esaim-cocv.org)
- J. Haslinger, T. Kozubek, K. Kunisch and G. Peichl, Shape optimization and fictitious domain approach for solving free boundary problems of Bernoulli type. (esaim-cocv.org)

- Article provided by Springer in its journal Computational Optimization and Applications . (repec.org)

- P. Posik, "Real-parameter optimization using the mutation step coevolution," in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '05) , pp. 872-879, 2005. (hindawi.com)
- J. Rönkkönen, S. Kukkonen, and K. V. Price, "Real-parameter optimization with differential evolution," in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '05) , pp. 506-513, September 2005. (hindawi.com)
- B. Yuan and M. Gallagher, "Experimental results for the special session on real-parameter optimization at CEC 2005: a simple, continuous EDA," in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '05) , pp. 1792-1799, September 2005. (hindawi.com)
- A. Sinha, S. Tiwari, and K. Deb, "A population-based, steady-state procedure for real-parameter optimization," in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '05) , pp. 514-521, September 2005. (hindawi.com)

- OLIVEIRA-LOPES, L. C. and MURATA, V.V. . A novel hybrid optimization algorithm for diferential-algebraic control problems . (scielo.br)
- Chapter three discusses deterministic/mathematical and optimization models evolving from differential equations, difference equations, algebraic models, power function models, input-output models and pathway models. (foyles.co.uk)
- Here initial value problems for large systems of differential-algebraic equations (DAEs) have to be solved. (wias-berlin.de)

- A. R. Hedar and M. Fukushima, Derivative - free filter simulated annealing method for constrained continuous global optimization,, Journal of Global Optimization , 35 (2006), 521. (aimsciences.org)
- I'm not sure what method you've used to generate these sample points, but I believe MOE implements EGO (Jones, "Efficient Global Optimization of Expensive Black-Box Functions," Journal of Global Optimization , 1998) for parameter estimation, which is highly regarded as a global optimization algorithm. (stackexchange.com)

- The performance of the presented framework is compared to that of existing techniques for constrained derivative-free optimization. (springer.com)

- The comparisons between CPLEX, IWO and genetic algorithm (GA) shows that the performance of the IWO algorithm is much better than the older algorithms and can be considered as an alternative to algorithms, such as GA in product portfolio optimization problems. (aimsciences.org)
- A. A. El-Fergany, "Solution of Economic Load Dispatch Problem with Smooth and Non-Smooth Fuel Cost Functions Including Line Losses Using Genetic Algorithm," International Journal of Computer and Electrical Engineering, Vol. 3, No. 5, pp. 706-710, doi:10.7763/IJCEE.2011.V3.407, Oct. 2011. (koreascience.or.kr)
- Simulated Annealing can be said to be the ancestor of Genetic Algorithms, with it's less constrained methodology, it's specification of criterion for the progress of the optimization procedure and the application of random shocks. (fu-lu-shou.net)
- Using diploid representation with dominance scheme is one of the approaches developed for genetic algorithms to address dynamic optimization problems. (dmu.ac.uk)

- Two cases were considered: the minimization of earliness-tardiness penalties in a single machine scheduling and the permutation flow shop problem. (igi-global.com)
- By convention, the standard form defines a minimization problem. (wikipedia.org)
- In mathematics, conventional optimization problems are usually stated in terms of minimization. (wikipedia.org)

- Since problem size increases with the size of the time series, synthesis of energy systems leads to complex optimization problems. (frontiersin.org)

- Numerical results of the structural design optimization problems are reported and compared. (aimsciences.org)
- Open the Response Optimization tool to configure and run design optimization problems interactively. (mathworks.com)

- Formulations and valid inequalities of the node capacitated graph partitioning problem, Mathematical Programming 74A - Ferreira, Martin, et al. (psu.edu)
- Optimization Online is supported by the Mathematical Programming Society . (optimization-online.org)
- In mathematics , computer science and operations research , mathematical optimization (alternatively spelled optimisation ) or mathematical programming is the selection of a best element (with regard to some criterion) from some set of available alternatives. (wikipedia.org)
- Such a formulation is called an optimization problem or a mathematical programming problem (a term not directly related to computer programming , but still in use for example in linear programming - see History below). (wikipedia.org)

- In mathematics and computer science, an optimization problem is the problem of finding the best solution from all feasible solutions. (wikipedia.org)
- At a very high level, a combinatorial optimization problem amounts to finding a solution with minimum or maximum cost among a large number of feasible solutions. (umd.edu)

- This paper focuses on modification of basic artificial algae algorithm (AAA) for solving binary optimization problems by using a new solution update rule because the agents in AAA work on continuous solution space. (springer.com)
- Optimization in Solving Elliptic Problems focuses on one of the most interesting and challenging problems of computational mathematics - the optimization of numerical algorithms for solving elliptic problems. (routledge.com)
- Optimization in Solving Elliptic Problems describes the construction of computational algorithms resulting in the required accuracy of a solution and having a pre-determined computational complexity. (routledge.com)
- Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems. (hindawi.com)
- Meanwhile, Toksari [ 5 ] developed an ACO algorithm for solving UGO problems. (hindawi.com)
- Finally, Kelsey and Timmis [ 6 ] presented an AIA method based on the clonal selection principle for solving 12 UGO problems. (hindawi.com)
- Based on the smoothed problem, an algorithm for solving COP is proposed and some preliminary numerical results indicate that the algorithm is quite promising. (repec.org)
- The biological inspired optimization techniques have proven to be powerful tools for solving scheduling problems. (igi-global.com)
- The book contains two well-performed case studies, which include thorough analysis of complexity, modelling of problems, design and implementation of solving algorithms and their verification, testing and comparison. (springer.com)
- Approaches for performing simulation optimization for solving a constrained optimization problem are generally disclosed. (freepatentsonline.com)
- In this paper, we describe the Globalizer software system for solving the global optimization problems. (aimsciences.org)
- K. Barkalov , V. Gergel and I. Lebedev , Use of Xeon Phi coprocessor for solving global optimization problems, LNCS , 9251 (2015), 307-318. (aimsciences.org)
- K. Barkalov , V. Gergel , I. Lebedev and A. Sysoev , Solving the global optimization problems on heterogeneous cluster systems, CEUR Workshop Proceedings , 1482 (2015), 411-419. (aimsciences.org)
- The level set function is evolved by solving the Hamilton-Jacobian equation, and numerical test is carried out for capillary to demonstrate the robustness of the proposed topology optimization method. (global-sci.org)
- These optimization techniques converge very fast and are very efficient for solving optimization problems with very large scale feasible domains. (optimization-online.org)
- But these optimization techniques are not effective in solving the numerical optimization problems with long narrow feasible domains. (optimization-online.org)
- In this paper, we calculate probabilities of the appearance of a better solution than the current one on each iteration, and on the performance of the algorithm we create good conditions for its appearance, we improve the algorithm to search and determine the correct values for each digit from left to right of variables of a solution for solving numerical optimization problems with long narrow feasible domains. (optimization-online.org)
- Thong N. H. (11/2012), "A new Search via Probability Algorithm for solving Engineering Optimization Problems", Journal of science, Special issue: Natural sciences and Technology, Ho Chi Minh city: University of Pedagogy, 40(74), pp. 23-33. (optimization-online.org)
- Thus proposed system brings robustness, efficiency, and effectiveness while solving MLCS problem. (springer.com)
- Blum C, Blesa MJ (2018) Hybrid techniques based on solving reduced problem instances for a longest common subsequence problem. (springer.com)
- It reflects human beings' intelligence in problem solving to some extent. (deepdyve.com)
- Granular computing (GrC) combines the multi-granularity thinking pattern of human intelligence with problem solving mode to deal with big data. (deepdyve.com)
- Based on the related notions and characteristics of GrC, this paper reviews the previous studies of GrC in three progressive levels: granularity optimization, granularity conversion and multi-granularity joint problem solving. (deepdyve.com)
- R. Goncalves, C. Almeida, J. Kuk, and M. Delgado, "Solving Economic Load Dispatch Problem by Natural Computing Intelligent Systems," 15th International Conference on Intelligent System Applications to Power Systems (ISAP), pp. 1-6, 8-12, doi:10.1109/ISAP.2009.5352843, Nov. 2009. (koreascience.or.kr)
- K. Zare, M. T. Haque, and E. Davoodi, "Solving Non-convex Economic Dispatch Problem with Valve Point Effects using Modified Group Search Optimizer Method," Electrical Power Systems Research, Vol. 84, No. 1, pp. 83-89, doi:10.1016 /j.epsr.2011.10.004, Mar. 2012. (koreascience.or.kr)
- The robustness and efficiency of the presented method are evaluated by using standard mathematical functions and hy solving a practical engineering problem. (unesp.br)

- fzhangcode/global_optimization: It is a global optimization algorithm for a sparse mixed-membership matrix factorization problem for molecular subtypes classification. (github.com)
- It is a global optimization algorithm for a sparse mixed-membership matrix factorization problem for molecular subtypes classification. (github.com)

- In this paper, we propose an interior-point method for linearly constrained optimization problems (possibly nonconvex). (arxiv.org)

- K. Barkalov , A. Polovinkin , I. Meyerov , S. Sidorov and N. Zolotykh , SVM regression parameters optimization using parallel global search algorithm, LNCS , 7979 (2013), 154-166. (aimsciences.org)
- Role of parameters in ED problems technique is essential to estimate the coefcients of fuel cost and emission functions. (scribd.com)
- Economic Dispatch (ED) ensures the economical operation of power system that requires the accurate fuel inputoutput charac- Existing parameters estimation procedures for ED problem teristic of generators which may be linear or non-linear. (scribd.com)
- A dialog to select model parameters for optimization opens. (mathworks.com)
- estimation and optimization of model parameters. (wikipedia.org)

- A. Ben-Tal , L. El Ghaoui and A. Nemirovski , Robust Optimization , Princeton University Press, 2009. (aimsciences.org)

- This paper presents topology optimization of capillary, the typical two-phase flow with immiscible fluids, where the level set method and diffuse-interface model are combined to implement the proposed method. (global-sci.org)
- Then the topology optimization problem for the two-phase flow is constructed for the cost functional with general formulation. (global-sci.org)

- Problem denitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization," Tech. Rep., Nanyang Technological University, 2005. (hindawi.com)

- D. Famularo , P. Pugliese and Y. D. Sergeyev , A global optimization technique for checking parametric robustness, Automatica , 35 (1999), 1605-1611. (aimsciences.org)

- This paper proposes a valve-point optimization (VPO) algorithm for economic load dispatch problem with non-convex fuel cost functions. (koreascience.or.kr)

- Our 3D EM simulation software is user-friendly and enables you to choose the most appropriate method for the design and optimization of devices operating in a wide range of frequencies. (cst.com)
- Identify the actions that will produce the best results, while operating within resource limitations and other relevant restrictions, using a powerful array of optimization, simulation and project scheduling techniques. (sas.com)

- His areas of interest and expertise are in Analysis and Design of Composite Aerospace Structures, Structural Optimization, Uncertainty Quantification, and Reliability Based Design. (ucsd.edu)