Evolutionary Computation 2

Thomas Baeck

Evolutionary Computation 2: Advanced Algorithms and Operators expands upon the basic ideas underlying evolutionary algorithms. The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the implementation of evolutionary algorithms. It is intended to be used by individual researchers and students in the expanding field of evolutionary computation.

FITNESS EVALUATIONIntroduction to fitness evaluationEncoding and decoding functionsCompetitive fitness evaluationComplexity-based fitness evaluationMultiobjective optimization CONSTRAINT-HANDLING TECHNIQUESIntroduction to constraint-handling techniquesPenalty functionsDecodersRepair algorithmsConstraint-preserving operatorsOther constraint-handling methodsConstraint-satisfaction problems POPULATION STRUCTURESNiching methodsSpeciation methodsIsland (migration) models: evolutionary algorithms based on punctuated equilibriaDiffusion (cellular) models ADVANCED TECHNIQUES IN EVOLUTIONARY COMPUTATIONPopulation sizingMutation parametersRecombination parametersParameter controlSelf-adaptationMeta-evolutionary approachesCoevolutionary algorithms IMPLEMENTATION OF EVOLUTIONARY ALGORITHMSEfficient implementation of algorithmsComputation time of evolutionary operatorsHardware realizations of evolutionary algorithmsINDEX