Empirical study of the effect of variable correlation on grouping in Cooperative Coevolutionary Evolutionary Algorithms

Cooperative Coevolutionary Evolutionary Algorithm is an extension of conventional Evolutionary Algorithm: it implements the idea of divide and conquer by dividing the whole set of variables into several subsets (groups), and evolve each subset independently with a certain optimizer. How to group the variables effectively have been studied by several researchers. Quite a number of variable grouping strategies have been proposed, in most of which, the correlation among variables is considered as the most important factor for guiding grouping, although its legitimacy has not been investigated comprehensively. In this paper an empirical analysis is conducted to testify the legitimacy of assumption that the correlation among variables is an important factor for variable grouping. The experiment results show that, although in some situation, the performance of random grouping is better than that of grouping based on the correct correlation knowledge, the variable correlation is obviously an important factor affecting the performance of the grouping strategies.