Search results for "Genetic algorithm"

showing 10 items of 834 documents

Behavior adaptation and selection.

2010

6 pages; The evolutionary approach to behavior is concerned with the evolutionary origin and adaptive function of behavioral traits. Like any other part of the phenotype, behavior can be shaped by natural selection to produce adaptations. However, behavior often shows large phenotypic variation and flexibility, and can be both – subject to selection and a major agent of selection. Therefore, the study of adaptation and evolution of behavior is a particularly complex one, involving a wide range of methodologies and techniques, including mathematical modeling, comparative methods, phenotypic engineering, quantitative genetics, genetic dissection, and artificial selection.

Flexibility (engineering)Optimization[ SDE.BE ] Environmental Sciences/Biodiversity and EcologyNatural selectionQuantitative geneticsEvolutionArtificial selectionNatural selectionQuantitative geneticsVariation (game tree)BiologyComparative methodEvolutionarily stable strategyAdaptive behaviour[ SDV.BID.EVO ] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE]Sexual selectionEvolutionary biologySexual selectionEvolutionary stable strategy[ SDV.EE.IEO ] Life Sciences [q-bio]/Ecology environment/SymbiosisAdaptationAdaptationSelection (genetic algorithm)
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Real-Time Routing Selection in Flexible Manufacturing Systems

1993

Routing flexibility is one of the main peculiarities of Flexible Manufacturing Systems. This paper proposes three methods for real-time routing selection. The first one makes decisions comparing the current workload of machines in each alternative path. The second method considers the current workloads at the bottleneck machines in each allowed route. The third approach makes real-time decisions minimizing a merit index that represents a measure of the still required resource amount. The index is computed by short discrete-event simulation runs. Some case studies evaluate and compare the proposed approaches.

Flexibility (engineering)Resource (project management)Computer-integrated manufacturingComputer scienceDistributed computingPath (graph theory)WorkloadRouting (electronic design automation)Selection (genetic algorithm)Bottleneck
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Life History Trade-Offs and Relaxed Selection Can Decrease Bacterial Virulence in Environmental Reservoirs

2012

Pathogen virulence is usually thought to evolve in reciprocal selection with the host. While this might be true for obligate pathogens, the life histories of opportunistic pathogens typically alternate between within-host and outside-host environments during the infection-transmission cycle. As a result, opportunistic pathogens are likely to experience conflicting selection pressures across different environments, and this could affect their virulence through life-history trait correlations. We studied these correlations experimentally by exposing an opportunistic bacterial pathogen Serratia marcescens to its natural protist predator Tetrahymena thermophila for 13 weeks, after which we meas…

Food ChainEvolutionary ProcessesScienceVirulenceParallel EvolutionPathogenesisEnvironmentBiologyForms of EvolutionMicrobiologyDivergent EvolutionTetrahymena thermophilaMicrobial Ecology03 medical and health sciencesNatural Selectionexperimental evolutionSelection GeneticAdaptationBiologyMicrobial PathogensPathogenSerratia marcescensSelection (genetic algorithm)030304 developmental biologyGeneticsEvolutionary Biology0303 health sciencesMultidisciplinaryEcologyObligate030306 microbiologyHost (biology)Mechanism (biology)QRAdaptation PhysiologicalBiological EvolutionBacterial PathogensvirulenceEvolutionary EcologyMicrobial EvolutionBacterial pigmentMedicineta1181AdaptationResearch ArticlePLoS ONE
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An integrated fuzzy cells-classifier

2007

This paper introduces a genetic algorithm able to combine different classifiers based on different distance functions. The use of a genetic algorithm is motivated by the fact that the combination phase is based on the optimization of a vote strategy. The method has been applied to the classification of four types of biological cells, results show an improvement of the recognition rate using the genetic algorithm combination strategy compared with the recognition rate of each single classifier.

Fuzzy classificationMeta-optimizationbusiness.industryPopulation-based incremental learningFuzzy setPattern recognitionMultiple classifiersMachine learningcomputer.software_genreFuzzy logicClusteringComputingMethodologies_PATTERNRECOGNITIONGenetic algorithmSignal ProcessingGenetic algorithmClassifier fusionFuzzy setComputer Vision and Pattern RecognitionArtificial intelligenceCluster analysisbusinessClassifier (UML)computerMathematics
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A genetic integrated fuzzy classifier

2005

This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.

Fuzzy classificationNeuro-fuzzyComputer scienceFuzzy setMachine learningcomputer.software_genreClassification Classifier Ensemble Evolutionary Algorithms.Artificial IntelligenceRobustness (computer science)Genetic algorithmCluster analysisAdaptive neuro fuzzy inference systemLearning classifier systemSettore INF/01 - InformaticaArtificial neural networkStructured support vector machinebusiness.industryPattern recognitionQuadratic classifierSupport vector machineComputingMethodologies_PATTERNRECOGNITIONSignal ProcessingMargin classifierFuzzy set operationsComputer Vision and Pattern RecognitionArtificial intelligencebusinesscomputerClassifier (UML)SoftwarePattern Recognition Letters
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Combining one class fuzzy KNN’s

2007

This paper introduces a parallel combination of N > 2 one class fuzzy KNN (FKNN) classifiers. The classifier combination consists of a new optimization procedure based on a genetic algorithm applied to FKNN’s, that differ in the kind of similarity used. We tested the integration techniques in the case of N = 5 similarities that have been recently introduced to face with categorical data sets. The assessment of the method has been carried out on two public data set, the Masquerading User Data (www.schonlau.net) and the badges database on the UCI Machine Learning Repository (http://www.ics.uci.edu/~mlearn/). Preliminary results show the better performance obtained by the fuzzy integration …

Fuzzy classificationSettore INF/01 - InformaticaComputer sciencebusiness.industryPattern recognitioncomputer.software_genreFuzzy logicClassifier combinationComputingMethodologies_PATTERNRECOGNITIONGenetic algorithmFuzzy set operationsData miningArtificial intelligencebusinessfuzzy classificationCategorical variablecomputerFuzzy knnClassifier (UML)
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Optimal slope units partitioning in landslide susceptibility mapping

2020

In landslide susceptibility modeling, the selection of the mapping units is a very relevant topic both in terms of geomorphological adequacy and suitability of the models and final maps. In this paper, a test to integrate pixels and slope units is presented. MARS (Multivariate Adaptive Regression Splines) modeling was applied to assess landslide susceptibility based on a 12 predictors and a 1608 cases database. A pixel-based model was prepared and the scores zoned into 10 different types of slope units, obtained by differently combining two half-basin (HB) and four landform classification (LCL) coverages. The predictive performance of the 10 models were then compared to select the best perf…

G3180-9980010504 meteorology & atmospheric sciencesGeography Planning and DevelopmentLand managementland managementimera settentrionale river basin (sicily)Mars Exploration ProgramLandslide susceptibility010502 geochemistry & geophysics01 natural sciencesmapping unitsImera Settentrionale river basin (Sicily) land management Landslide susceptibility mapping units MARSMapsEarth and Planetary Sciences (miscellaneous)landslide susceptibilitymarsCartographyGeologySelection (genetic algorithm)0105 earth and related environmental sciences
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An Innovative Structural Dynamic Identification Procedure Combining Time Domain OMA Technique and GA

2022

In this paper an innovative and simple Operational Modal Analysis (OMA) method for structural dynamic identification is proposed. It combines the recently introduced Time Domain–Analytical Signal Method (TD–ASM) with the Genetic Algorithm (GA). Specifically, TD–ASM is firstly employed to estimate a subspace of candidate modal parameters, and then the GA is used to identify the structural parameters minimizing the fitness value returned by an appropriately introduced objective function. Notably, this method can be used to estimate structural parameters even for high damping ratios, and it also allows one to identify the Power Spectral Density (PSD) of the structural excitat…

Genetic AlgorithmPower Spectral Densitystructural dynamic identificationStructural Health MonitoringArchitecturecorrelation functionBuilding and Constructioncorrelation function; Power Spectral Density; Structural Health Monitoring; Hilbert transform; Genetic Algorithm; structural dynamic identification; Operational Modal AnalysisSettore ICAR/08 - Scienza Delle CostruzioniHilbert transformOperational Modal AnalysiCivil and Structural EngineeringBuildings; Volume 12; Issue 7; Pages: 963
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Genetic variability at neutral markers, quantitative trait loci and trait in a subdivided population under selection

2003

Abstract Genetic variability in a subdivided population under stabilizing and diversifying selection was investigated at three levels: neutral markers, QTL coding for a trait, and the trait itself. A quantitative model with additive effects was used to link genotypes to phenotypes. No physical linkage was introduced. Using an analytical approach, we compared the diversity within deme (HS) and the differentiation (FST) at the QTL with the genetic variance within deme (VW) and the differentiation (QST) for the trait. The difference between FST and QST was shown to depend on the relative amounts of covariance between QTL within and between demes. Simulations were used to study the effect of se…

Genetic Markers0106 biological sciencesGenotypeQuantitative Trait LociPopulation[SDV.GEN] Life Sciences [q-bio]/GeneticsQuantitative trait locusBiology010603 evolutionary biology01 natural sciences03 medical and health sciencesFamily-based QTL mappingGeneticsComputer SimulationGenetic variabilitySelection Genetic10. No inequalityeducationSelection (genetic algorithm)ComputingMilieux_MISCELLANEOUS030304 developmental biologyDemeGenetics0303 health scienceseducation.field_of_study[SDV.GEN]Life Sciences [q-bio]/GeneticsModels GeneticDisruptive selectionGenetic VariationGenetic architectureGenetics PopulationPhenotypeEvolutionary biologyResearch Article
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Optimal positioning of water quality sensors in water distribution networks: Comparison of numerical and experimental results

2017

In the water distribution networks, a deliberate or accidental contamination causes loss of water quality; the implementation of a real-time sensor network is essential to promptly detect the event of contamination. To achieve the optimum positioning of the probes, to reduce the cost of the instrumentation and maintenance, and obtaining, at the same time, a reliable monitoring of the system, optimization techniques are widely applied. In the present study, a numerical optimisation approach was compared with the results of an experimental campaign. The optimization problem is formulated in accordance with literature stateof- the-art, using the genetic algorithm NSGA-II coupled with a hydraul…

Genetic algorithmwater distribution networksoptimal positioning of sensor
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