Sequential parameter optimization applied to self-adaptation for binary-coded evolutionary algorithms

Adjusting algorithm parameters to a given problem is of crucial importance for performance comparisons as well as for reliable (first) results on previously unknown problems, or with new algorithms. This also holds for parameters controlling adaptability features, as long as the optimization algorithm is not able to completely self-adapt itself to the posed problem and thereby get rid of all parameters. We present the recently developed sequential parameter optimization (SPO) technique that reliably finds good parameter sets for stochastically disturbed algorithm output. SPO combines classical regression techniques and modern statistical approaches for deterministic algorithms as Design and Analysis of Computer Experiments (DACE). Moreover, it is embedded in a twelve-step procedure that targets at doing optimization experiments in a statistically sound manner, focusing on answering scientific questions. We apply SPO to a question that did not receive much attention yet: Is self-adaptation as known from real-coded evolution strategies useful when applied to binary-coded problems? Here, SPO enables obtaining parameters resulting in good performance of self-adaptive mutation operators. It thereby allows for reliable comparison of modified and traditional evolutionary algorithms, finally allowing for well founded conclusions concerning the usefulness of either technique.