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Randomized search heuristics such as evolutionary algorithms, genetic algorithms, evolution strategies, ant colony and particle swarm optimization turn out to be highly successful for optimization in practice. The theory of randomized search heuristics, which has been growing rapidly in the last five years, also attempts to explain the success of the methods in practical applications. This book covers both classical results and the most recent theoretical developments in the field of randomized search heuristics such as runtime analysis, drift analysis and convergence. Each chapter provides an overview of a particular domain and gives insights into the proofs and proof techniques of more specialized areas. Open problems still remain widely in randomized search heuristics being a relatively young and vast field. These problems and directions for future research are addressed and discussed in this book. The book will be an essential source of reference for experts in the domain of randomized search heuristics and also for researchers who are involved or ready to embark in this field. As an advanced textbook, graduate students will benefit from the comprehensive coverage of topics.
List of contents
Tail Inequalities (B Doerr); No-Free-Lunch Theorems (D Whitley); GA-Theory; Runtime Analyses of EAs, in particular, the Drift Analysis Method (P Oliveto et al.); EAs for Combinatorial Optimization Problems, in particular, Good Representations (D Johannsen); Ant Colony Optimization; Evolution Strategies: Lower Bounds and Complexity Analysis; Evolution Strategies: Theory of Markov Chains for Log-Linear Convergence (A Auger & N Hansen); Particle Swarm Optimization (C Witt); Probabilistic Model Building; Learning Classifiers.
