Learning Features for Discriminative Behavior Analysis of Evolutionary Algorithms via Slow Feature Analysis
Evolutionary algorithms (EAs) are a kind of stochastic optimization methods, which have been testified to be powerful in solving many real world hard problems in past decades. But till now, we are still short of effective methods to represent and investigate their collective behaviors in various environments, which are very useful for researchers and engineers in Evolutionary Computation to understand the algorithms better. This paper is a preliminary effort to tackle above issue. We attempt to analyze the generation wise collective behavior of EAs via an approach called feature learning. An unsupervised feature learning framework based on Slow Feature Analysis (SFA) is presented to extract discriminative features from the generation wise collective behavior data of several EAs on various fitness landscapes, with the purpose of finding out whether there exist differences between the searching behavior of different EAs running on the same fitness landscape; and whether there are differences between the behavior of one algorithm running on different fitness landscapes. Besides, the relationship between the fitness landscape and the searching behavior of EA is also studied. In the experiments, several typical EAs and classical benchmark functions with typical landscapes are selected as the study subjects. The collective behaviors of various EAs are visualized and compared in the extracted feature space.
GECCO