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. © Springer-Verlag Berlin Heidelberg 2004.
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CITATION STYLE
De Jong, E. D. (2004). The incremental pareto-coevolution archive. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3102, 525–536. https://doi.org/10.1007/978-3-540-24854-5_55
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