The Practical Handbook of Genetic Algorithms Applications, Second Edition

Rapid developments in the field of genetic algorithms along with the popularity of the first edition precipitated this completely revised, thoroughly updated second edition of The Practical Handbook of Genetic Algorithms. Like its predecessor, this edition helps practitioners stay up to date on recent developments in the field and provides material they can use productively in their own endeavors. For this edition, the editor again recruited authors at the top of their field and from a cross section of academia and industry, theory and practice. Their contributions detail their own research, new applications, experiment results, and recent advances. Among the applications explored are scheduling problems, optimization, multidimensional scaling, constraint handling, and feature selection and classification. The science and art of GA programming and application has come a long way in the five years since publication of the bestselling first edition. But there still is a long way to go before its bounds are reached-we are still just scratching the surface of GA applications and refinements. By introducing intriguing new applications, offering extensive lists of code, and reporting advances both subtle and dramatic, The Practical Handbook of Genetic Algorithms is designed to help readers contribute to scratching that surface a bit deeper.

MODEL BUILDING, MODEL TESTING, AND MODEL FITTINGUses of Genetic AlgorithmsQuantitative ModelsAnalytical OptimizationIterative Hill-Climbing TechniquesAssay Continuity in a Gold ProspectConclusionsCOMPACT FUZZY MODELS AND CLASSIFIERS THROUGH MODEL REDUCTION AND EVOLUTIONARY OPTIMIZATIONFuzzy ModelingTransparency and Accuracy of Fuzzy ModelsGenetic AlgorithmsCrossover OperatorsExamplesTS Singleton ModelTS Linear ModelConclusionON THE APPLICATION OF REORGANIZATION OPERATORS FOR SOLVING A LANGUAGE RECOGNITION PROBLEMPerformance Across a New Problem SetReorganization OperatorsThe ExperimentationData Obtained from the ExperimentationGeneral Evaluation CriteriaEvaluationConclusions and Further DirectionsUSING GA TO OPTIMIZE THE SELECTION AND SCHEDULING OF ROAD PROJECTSIntroductionFormulation of the Genetic AlgorithmMapping the GA String into a Project Schedule and Computing the FitnessResultsConclusions: Scheduling Interactive Road Projects by GADECOUPLED OPTIMIZATION OF POWER ELECTRONICS CIRCUITS USING GENETIC ALGORITHMSIntroductionDecoupled Regulator ConfigurationFitness Function for FNSteps of OptimizationDesign ExampleConclusionsFEATURE SELECTION AND CLASSIFICATION IN THE DIAGNOSIS OF CERVICAL CANCERIntroductionFeature SelectionFeature Selection by Genetic AlgorithmDeveloping a Neural Genetic ClassifierValidation of the AlgorithmParameterization of the GAExperiments with the Cell Image Data SetALGORITHMS FOR MULTIDIMENSIONAL SCALINGIntroductionMultidimensional Scaling Examined in more DetailA Genetic Algorithm for Multidimensional ScalingExperimental ResultsThe Computer ProgramUsing the Extend ProgramGENETIC ALGORITHM-BASED APPROACH FOR TRANSPORTATION OPTIMIZATION PROBLEMSGAs-Based Solution Approach for Transport ModelsGAs-Based Calibration Approach for Transport ModelsConcluding RemarksSOLVING JOB-SHOP SCHEDULING PROBLEMS BY MEANS OF GENETIC ALGORITHMSIntroductionThe Job Shop Scheduling Constraint Satisfaction ProblemThe Genetic AlgorithmFitness RefinementHeuristic Initial PopulationExperimental ResultsConclusionsAPPLYING THE IMPLICIT REDUNDANT REPRESENTATION GENETIC ALGORITHM IN AN UNSTRUCTURED PROBLEM DOMAINIntroductionMotivation for Frame Synthesis Research Notes in Mathematics series The Implicit Redundant Representation of Genetic AlgorithmThe IRR Genotype/Phenotype RepresentationApplying the IRR GA to Frame Design Synthesis in an Unstructured DomainIRR GA Fitness Evaluation of Frame Design Synthesis AlternativesDiscussion of the Genetic Control Operators Used by the IRR GAResults of the Implicit Redundant Representation Frame Synthesis TrialsHOW TO HANDLE CONSTRAINTS WITH EVOLUTIONARY ALGORITHMSIntroductionConstraints Handling in EAsEvolutionary CSP SolversDiscussionAssessment of Eas for CSPsConclusionAN OPTIMIZED FUZZY LOGIC CONTROLLER FOR ACTIVE POWER FACTOR CORRECTOR USING GENETIC ALGORITHMIntroductionFLC for the Boost RectifierOptimization of FLC by the Genetic AlgorithmIllustrative ExampleConclusionsMULTILEVEL FUZZY PROCESS CONTROL OPTIMIZED BY GENETIC ALGORITHMIntroductionIntelligent ControlMultilevel ControlOptimizing Aided by Genetic AlgorithmLaboratory Cascaded PlantMultilevel Control using Genetic AlgorithmFuzzy Multilevel Coordinated ControlConclusionsEvolving Neural Networks for Cancer RadiotherapyEVOLVING NEURAL NETWORKS FOR CANCER RADIOTHERAPYIntroduction and Chapter OverviewAn Introduction to RadiotherapyEvolutionary Artificial Neural NetworksRadiotherapy Treatment Planning with EANNsSummaryDiscussion and Future Work