Theory of Randomized Search Heuristics

Abstract

Randomized search heuristics such as evolutionary algorithms, evolution strategies, ant colony optimizers etc. are optimization algorithms that can be applied to a wide class of problems ranging from combinatorial to continuous optimization. They are popular in practice because they are generally easy to implement, their application requires little assumptions on the function to be optimized and provide solutions to a problem in a comparatively short time. Their theoretical analysis has gained momentum over the past decade. Runtime analysis of various randomized search heuristics, using and adapting methods from the analysis of randomized algorithms, has emerged as a new line of research, and general theoretical tools that can be applied to different algorithms have been developed. In 2007, a theory track dedicated to analyses of this kind was established at the Genetic and Evolutionary Computation Conference (GECCO), one of the largest conference on randomized search heuristics. This special issue on Theory of Randomized Search Heuristics of Algorithmica contains papers that are extensions or follow-ups of papers from the theory track of the GECCO held July 7–11, 2010 in Portland, Oregon. Each article has undergone a new rigorous journal review process from which four papers have been selected. They contribute to improve our theoretical knowledge about randomized search heuristics as well as they enhance general tools and techniques for their analysis. The paper Black-Box Search by Unbiased Variation by Lehre and Witt (doi:10. 1007/s00453-012-9616-8) reconsiders and refines the black-box model of randomized search heuristics originally proposed by Droste, Jansen and Wegener (Theory