An Analysis of Cooperative Coevolutionary Algorithms

Abstract

Coevolutionary algorithms behave in very complicated, often quite counterintuitive ways. Researchers and practitioners have yet to understand why this might be the case, how to change their intuition by understanding the algorithms better, and what to do about the differences. Unfortunately, there is little existing theory available to researchers to help address these issues. Further, little empirical analysis has been done at a component level to help understand intrinsic differences and similarities between coevolutionary algorithms and more traditional evolutionary algorithms. Finally, attempts to categorize coevolution and coevolutionary behaviors remain vague and poorly defined at best. The community needs directed investigations to help practitioners understand what particular coevolutionary algorithms are good at, what they are not, and why.

This dissertation improves our understanding of coevolution by posing and answering the question: "Are cooperative coevolutionary algorithms (CCEAs) appropriate for static optimization tasks?" Two forms of this question are "How long do they take to reach the global optimum" and "How likely are they to get there?" The first form of the question is addressed by analyzing their performance as optimizers, both theoretically and empirically. This analysis includes investigations into the effects of coevolution-specific parameters on optimization performance in the context of particular properties of potential problem domains. The second leg of this dissertation considers the second form of the question by looking at the dynamical properties of these algorithms, analyzing their limiting behaviors again from theoretical and empirical points of view. Two common cooperative coevolutionary pathologies are explored and illustrated, in both formal and practical settings. The result is a better understanding of, and appreciation for, the fact that CCEAs are not generally appropriate for the task of static, single-objective optimization. In the end a new view of the CCEA is offered that includes analysis-guided suggestions for how a traditional CCEA might be modified to be better suited for optimization tasks, or might be applied to more appropriate tasks, given the nature of its dynamics.

Downloading the disseration:

• Download the University-formatted version (not recommended)
• Download the printer friendly version (recommended)