AI Article Synopsis
- Coevolutionary algorithms evaluate individuals based on their performance against evolving opponents, unlike standard evolutionary algorithms that use a fixed fitness metric.
- One major challenge in coevolution is disengagement, where individuals stop evolving effectively, along with other issues like cycling and over-focusing.
- The proposed solution involves selecting for individuals with lower "virulence," similar to host-parasite dynamics, which can paradoxically enhance the effectiveness of coevolutionary processes.
Article Abstract
While standard evolutionary algorithms employ a static, absolute fitness metric, coevolutionary algorithms assess individuals by their performance relative to populations of opponents that are themselves evolving. Although this arrangement offers the possibility of avoiding long-standing difficulties such as premature convergence, it suffers from its own unique problems, cycling, over-focusing and disengagement. Here, we introduce a novel technique for dealing with the third and least explored of these problems. Inspired by studies of natural host-parasite systems, we show that disengagement can be avoided by selecting for individuals that exhibit reduced levels of "virulence", rather than maximum ability to defeat coevolutionary adversaries. Experiments in both simple and complex domains are used to explain how this counterintuitive approach may be used to improve the success of coevolutionary algorithms.
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| http://dx.doi.org/10.1162/106365604773955148 | DOI Listing |
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