Comparing parameter tuning methods for evolutionary algorithms

Abstract

Abstract — Tuning the parameters of an evolutionary algorithm (EA) to a given problem at hand is essential for good algorithm performance. Optimizing parameter values is, however, a non-trivial problem, beyond the limits of human problem solving.In this light it is odd that no parameter tuning algorithms are used widely in evolutionary computing. This paper is meant to be stepping stone towards a better practice by discussing the most important issues related to tuning EA parameters, describing a number of existing tuning methods, and presenting a modest experimental comparison among them. The paper is concluded by suggestions for future research – hopefully inspiring fellow researchers for further work. Index Terms — evolutionary algorithms, parameter tuning I. BACKGROUND AND OBJECTIVES Evolutionary Algorithms (EA) form a rich class of stochasti