Proceedings of the 2002 ACM symposium on Applied computing
Evolving Cellular Automata for Location Management in Mobile Computing Networks
IEEE Transactions on Parallel and Distributed Systems
Optimization as Side-Effect of Evolving Allelopathic Diversity
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Pareto Optimality in Coevolutionary Learning
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
The MaxSolve algorithm for coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Investigating the success of spatial coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Ideal Evaluation from Coevolution
Evolutionary Computation
A Monotonic Archive for Pareto-Coevolution
Evolutionary Computation
International Journal of Computer Applications in Technology
CrossNet: a framework for crossover with network-based chromosomal representations
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Coevolution of simulator proxies and sampling strategies for petroleum reservoir modeling
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
The paradox of the plankton: oscillations and chaos in multispecies evolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A mapping function to use cellular automata for solving MAS problems
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
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Coevolution, between a population of candidate solutions and a population of test cases, has received increasing attention as a promising biologically inspired method for improving the performance of evolutionary computation techniques. However, the results of studies of coevolution have been mixed. One of the seemingly more impressive results to date was the improvement via coevolution demonstrated by Juille and Pollack (1998) on evolving cellular automata to perform a classification task. Their study, however, like most other studies on coevolution, did not investigate the mechanisms giving rise to the observed improvements. In this paper, we probe more deeply into the reasons for these observed improvements and present empirical evidence that, in contrast to what was claimed by Juille and Pollack, much of the improvement seen was due to their "resource sharing" technique rather than to coevolution. We also present empirical evidence that resource sharing works, at least in part, by preserving diversity in the population.