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.
Preface; Contents; 1. Analyzing Randomized Search Heuristics: Tools from Probability Theory Benjamin Doerr; 2. Runtime Analysis of Evolutionary Algorithms for Discrete Optimization Peter S. Oliveto and Xin Yao; 3. Evolutionary Computation in Combinatorial Optimization Daniel Johannsen; 4. Theoretical Aspects of Evolutionary Multiobjective Optimization Dimo Brockho; 5. Memetic Evolutionary Algorithms Dirk Sudholt; 6. Simulated Annealing Thomas Jansen; 7. Theory of Particle Swarm Optimization Carsten Witt; 8. Ant Colony Optimization: Recent Developments in Theoretical Analysis Walter J. Gutjahr