ABSTRACT
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 EVALUATION Introduction to fitness evaluation Encoding and decoding functions Competitive fitness evaluation Complexity-based fitness evaluation Multiobjective optimization CONSTRAINT-HANDLING TECHNIQUES Introduction to constraint-handling techniques Penalty functions Decoders Repair algorithms Constraint-preserving operators Other constraint-handling methods Constraint-satisfaction problems POPULATION STRUCTURES Niching methods Speciation methods Island (migration) models: evolutionary algorithms based on punctuated equilibria Diffusion (cellular) models ADVANCED TECHNIQUES IN EVOLUTIONARY COMPUTATION Population sizing Mutation parameters Recombination parameters Parameter control Self-adaptation Meta-evolutionary approaches Coevolutionary algorithms IMPLEMENTATION OF EVOLUTIONARY ALGORITHMS Efficient implementation of algorithms Computation time of evolutionary operators Hardware realizations of evolutionary algorithms INDEX
