Abstract. Finding appropriate parameter values for Evolutionary Algorithms (EAs) is one of the persistent challenges of Evolutionary Computing. In recent publications we showed how the REVAC (Relevance Estimation and VAlue Calibration) method is capable to find good EA parameter values for single problems. Here we demonstrate that REVAC can also tune an EA to a set of problems (a whole test suite). Hereby we obtain robust, rather than problem-tailored, parameter values and an EA that is a 'generalist, rather than a 'specialist. The optimized parameter values prove to be different from problem to problem and also different from the values of the generalist. Furthermore, we compare the robust parameter values optimized by REVAC with the supposedly robust conventional values and see great differences. This suggests that traditional settings might be far from optimal, even if they are meant to be robust. Key words: parameter tuning, algorithm design, test suites, robustness Finding appropriate parameter values for evolutionary algorithms (EA) is one of the persisting grand challenges of the evolutionary computing (EC) field. As explained by Eiben et al. in [8] this challenge can be addressed before the run of the given EA (parameter tuning) or during the run (parameter control). In this paper we focus on parameter tuning, that is, we are seeking good parameter values off-line and use these values for the whole EA run. In today's practice, this tuning problem is usually 'solved' 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). Until recently, there were not many workable alternatives. However, by the developments over last couple of years now there are a number of tuning methods and corresponding software packages that enable EA practitioners to perform tuning without much effort. In particular, REVAC [10, 13] and SPOT [3, 5, 4] are well developed and documented. The main objective of this paper is to illustrate the advantage of using tuning algorithms in terms of improved EA performance. To this end, we will select a set
Smit and Eiben REF used REVAC to tune an EA to a set of problems (generalist) to obtain robust control parameters.
Parameter tuning of evolutionary algorithms: Generalist vs. specialist
Applications of Evolutionary Computation, volume 6024 of Lecture Notes in Computer Science
Computer Science