An empirical investigation of the user-parameters and performance of continuous PBIL algorithms [population-based incremental learning]

Evolutionary algorithms (EAs) are powerful methods for solving optimization problems, inspired by natural systems and incorporating population-based searching. Although the implementation of EAs is in many cases quite straightforward, it almost always involves making choices which can be viewed as assumptions regarding the nature of the problem to be solved. In this paper, one such choice is examined: the setting of user-defined parameters in three simple algorithms for solving unconstrained continuous optimization problems. Thre results agree with the notion that these algorithms are often robust to parameter settings, but also reveal interesting relationships between the parameters.