Parameter Tuning of Evolutionary Algorithms: Generalist vs. Specialist

Finding appropriate parameter values for Evolutionary Algorithms (EAs) is one of the persistent challenges of Evolutionary Computing. In recent publications we showed how the REVAG (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.