Emergent Geometric Organization and Informative Dimensions in Coevolutionary Algorithms

Emergent Geometric Organization and Informative Dimensions in Coevolutionary Algorithms A dissertation presented to the Faculty of the Graduate School of Arts and Sciences of Brandeis University, Waltham, Massachusetts by Anthony Bucci Coevolutionary algorithms vary entities which can play two or more distinct, interacting roles, with the hope of producing raw material from which a highlycapable composition can be constructed. Ranging in complexity from autodidactic checkers-learning systems to the evolution of competing agents in 3-d simulated physics, applications of these algorithms have proved both motivating and perplexing. Successful applications inspire further application, supporting the belief that a correctly implemented form of evolution by natural selection can produce highly-capable entities with minimal human input or intervention. However, the successes to date have generated limited insight into how to transfer success to other domains. On the other hand, failed applications leave behind a frustratingly opaque trace of misbehavior. In either case, the question of what worked or what went wrong is often left open. One impediment to understanding the dynamics of coevolutionary algorithms is that the interactive domains explored by these algorithms typically lack an explicit objective function. Such a function is a clear guide for judging the progress or regress of an algorithm. However, in the absence of an