Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Coevolutionary search among adversaries
Coevolutionary search among adversaries
Coevolutionary Learning: A Case Study
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Methods for Competitive Co-Evolution: Finding Opponents Worth Beating
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
Combating Coevolutionary Disengagement by Reducing Parasite Virulence
Evolutionary Computation
Evolutionary consequences of coevolving targets
Evolutionary Computation
Resource sharing and coevolution in evolving cellular automata
IEEE Transactions on Evolutionary Computation
The role of speciation in spatial coevolutionary function approximation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Co-evolutionary automatically defined functions in genetic programming
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Empirical analysis of the spatial genetic algorithm on small-world networks
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part III
Spatial co-evolution: quicker, fitter and less bloated
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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We investigate the results of coevolution of spatially distributed populations. In particular, we describe work in which a simple function approximation problem is used to compare different spatial evolutionary methods. Our work shows that, on this problem, spatial coevolution is dramatically more successful than any other spatial evolutionary scheme we tested. Our results support two hypotheses about the source of spatial coevolution's superior performance: (1) spatial coevolution allows population diversity to persist over many generations; and (2) spatial coevolution produces training examples ("parasites") that specifically target weaknesses in models ("hosts"). The precise mechanisms by which the combination of spatial embedding and coevolution produces these results are still not well understood.