Theory of Evolutionary Computation

“The eleven chapters of the book cover a remarkable range, and indeed it is astonishing to realise just how much has been achieved in the two decades since this approach to EA research began. … if you are working in the theory of evolutionary algorithms (or aspire to), then you really need this book. If you are interested in what theory has to say about specific topics, then you will also find much of importance.” (Jonathan E. Rowe, Genetic Programming and Evolvable Machines, Vol. 25 (2), 2024)

• Probabilistic Tools for the Analysis of Randomized Optimization Heuristics.- Drift Analysis.- Complexity Theory for Discrete Black-Box Optimization Heuristics.- Parameterized Complexity Analysis of Randomized Search Heuristics.- Analysing Stochastic Search Heuristics Operating on a Fixed Budget.- Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices.- Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments.- The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses.- Theory of Estimation-of-Distribution Algorithms.- Theoretical Foundations of Immune-Inspired Randomized Search Heuristics for Optimization.- Computational Complexity Analysis of Genetic Programming.