AI Article Synopsis
- Clearing is a resource allocation method that focuses on giving resources to the top individual in a niche, promoting diversity in evolutionary algorithms.
- The study demonstrates that a mutation-based evolutionary algorithm can optimize specific functions efficiently in polynomial time with the right conditions, while a genotypic distance function is less efficient.
- It concludes that clearing effectively finds optimal solutions for various functions in expected polynomial time and supports this with both theoretical and empirical analysis.
Article Abstract
Clearing is a niching method inspired by the principle of assigning the available resources among a niche to a single individual. The clearing procedure supplies these resources only to the best individual of each niche: the winner. So far, its analysis has been focused on experimental approaches that have shown that clearing is a powerful diversity-preserving mechanism. Using rigorous runtime analysis to explain how and why it is a powerful method, we prove that a mutation-based evolutionary algorithm with a large enough population size, and a phenotypic distance function always succeeds in optimising all functions of unitation for small niches in polynomial time, while a genotypic distance function requires exponential time. Finally, we prove that with phenotypic and genotypic distances, clearing is able to find both optima for and several general classes of bimodal functions in polynomial expected time. We use empirical analysis to highlight some of the characteristics that makes it a useful mechanism and to support the theoretical results.
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| http://dx.doi.org/10.1162/evco_a_00225 | DOI Listing |
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