Analysing co-evolution among artificial 3D creatures

This paper is concerned with the analysis of coevolutionary dynamics among 3D artificial creatures, similar to those introduced by Sims (1). Coevolution is subject to complex dynamics which are notoriously difficult to analyse. We introduce an improved analysis method based on Master Tournament matrices [2], which we argue is both less costly to compute and more informative than the original method. Based on visible features of the resulting graphs, we can identify particular trends and incidents in the dynamics of coevolution and look for their causes. Finally, considering that coevolutionary progress is not necessarily identical to global overall progress, we extend this analysis by cross-validating individuals from different evolutionary runs, which we argue is more appropriate than single-record analysis method for evaluating the global performance of individuals

The incremental pareto-coevolution archive

Abstract. Coevolution can in principle provide progress for problems where no accurate evaluation function is available. An important open question however is how coevolution can be set up such that progress can be ensured. Previous work has provided progress guarantees either for limited cases or using strict acceptance conditions that can result in stalling. We present a monotonically improving archive for the general asymmetric case of coevolution where learners and tests may be of distinct types, for which any detectable improvement can be accepted into the archive. The Incremental Pareto-Coevolution Archive is demonstrated in experiments.

Intransitivity in coevolution

Abstract. We review and investigate the current status of intransitivity as a potential obstacle in coevolution. Pareto-Coevolution avoids intransitivity by translating any standard superiority relation into a transitive Pareto-dominance relation. Even for transitive problems though, cycling is possible. Recently however, algorithms that provide monotonic progress for Pareto-Coevolution have become available. The use of such algorithms avoids cycling, whether caused by intransitivity or not. We investigate this in experiments with two intransitive test problems, and find that the IPCA and LAPCA archive methods establish monotonic progress on both test problems, thereby substantially outperforming the same method without an archive. Coevolution offers algorithms for problems where the performance of individuals can be evaluated using tests [1–7]. Since evaluation in coevolution is based on evolving individuals, coevolution setups can suffer from inaccurate evaluation, leading to problems such as over-specialization, Red Queen dynamics, an

A Population-Differential Method of Monitoring Success and Failure in Coevolution

The task of monitoring success and failure in coevolution is inherently di#cult, as domains need not have any external metric to measure performance. Past work on monitoring "progress" all strive to identify and measure success, but none attempt to identify failure. We suggest that this limitation is due to the reliance on a "best-of-generation" (BOG) memory mechanism, and propose an alternate "all-of-generation" (AOG) mechanism free of this limitation. Using AOG data, we propose a population-di#erential method for monitoring coevolution in arbitrary domains. With this method, we demonstrate the ability to profile and distinguish an assortment of coevolutionary successes and failures, including arms-race dynamics, disengagement, cycling, forgetting, and relativism

A game-theoretic approach for designing mixed mutation strategies

Abstract. Different mutation operators have been proposed in evolutionary programming. However, each operator may be efficient in solving a subset of problems, but will fail in another one. Through a mixture of various mutation operators, it is possible to integrate their advantages together. This paper presents a game-theoretic approach for designing evolutionary programming with a mixed mutation strategy. The approach is applied to design a mixed strategy using Gaussian and Cauchy mutations. The experimental results show the mixed strategy can obtain the same performance as, or even better than the best of pure strategies.