Theory of Evolutionary Computation

Abstract

Evolutionary algorithms are general-purpose randomized search heuristics. They have been successfully applied to many different kinds of search and optimization problems. They are popular in practice since they are easy to implement and easy to apply, and yet often find good solutions in reasonable time. This lead to an increased interest in gaining a thorough understanding of their strengths (and limitations). In the past decades, the theory of evolutionary algorithms has become an important and accepted field of research. This special issue Theory of Evolutionary Computation of Algorithmica contains expanded research from the theory track of the Genetic and Evolutionary Computation Conference and the International Conference on Parallel Problem Solving From Nature. All papers have been enhanced, extended, and undergone a critical journal reviewing process. The result is a collection of seven papers that all deal with theoretical aspects of evolutionary computation in a rigorous and accessible way. It contributes to the development of a better understanding of evolutionary algorithms, paving the way for a more informed application of these algorithms. Ten years after the first publication of a run-time analysis of the most simple evolutionary algorithm, the (1 + 1) evolutionary algorithm, Jens Jagerskupper reconsiders the result. By using the drift analysis method introduced by He and Yao in a sophisticated way, he obtains much more precise bounds on the expected optimization time. Drift analysis is also the topic of Pietro S. Oliveto’s and Carsten Witt’s article. They present a theorem that yields exponential lower bounds on the expected op-