Evolutionary Algorithm Parameters and Methods to Tune Them

I n this chapter we discuss the notion of Evolutionary Algorithm (EA) parameters and propose a distinction between EAs and EA instances, based on the type of parameters used to specify their details. Furthermore, we consider the most important aspects of the parameter tuning problem and give an overview of existing parameter tuning methods. Finally, we elaborate on the methodological issues involved here and provide recommendations for further development. 1 Background and Objectives Finding appropriate parameter values for evolutionary algorithms (EA) is one of the persisting grand challenges of the evolutionary computing (EC) field. In general, EC researchers and practitioners all acknowledge that good parameter values are essential for good EA performance. However, very little effort is spent on studying the effect of EA parameters on EA performance and on tuning them. In practice, parameter values are mostly selected by conventions (mutation rate should be low), ad hoc choices (why not use uniform crossover), and experimental comparisons on a limited scale (testing combinations of three different crossover rates and three different mutation rates). Hence, there is a striking gap between the widely acknowledged importance of good parameter values and the widely exhibited ignorance concerning principled approaches to tune EA parameters. To this end, it is important to recall that the problem of setting EA parameters is commonly divided into two cases, parameter tuning and parameter control [14]. A.E. Eiben Vrije Universiteit Amsterdam, e-mail: [email protected] S.K. Smit Vrije Universiteit Amsterdam e-mail: [email protected]