Title

Effective Diversity Maintenance In Deceptive Domains

Keywords

Deception; Diversity maintenance; NEAT; Novelty search

Abstract

Diversity maintenance techniques in evolutionary computation are designed to mitigate the problem of deceptive local optima by encouraging exploration. However, as problems become more difficult, the heuristic of fitness may become increasingly uninformative. Thus, simply encouraging geno-typic diversity may fail to much increase the likelihood of evolving a solution. In such cases, diversity needs to be directed towards potentially useful structures. A representative example of such a search process is novelty search, which builds diversity by rewarding behavioral novelty. In this paper the effectiveness of fitness, novelty, and diversity maintenance objectives are compared in two evolutionary robotics domains. In a biped locomotion domain, geno-typic diversity maintenance helps evolve biped control policies that travel farther before falling. However, the best method is to optimize a fitness objective and a behavioral novelty objective together. In the more deceptive maze navigation domain, diversity maintenance is ineffective while a novelty objective still increases performance. The conclusion is that while genotypic diversity maintenance works in well-posed domains, a method more directed by phenotypic information, like novelty search, is necessary for highly deceptive ones. Copyright © 2013 ACM.

Publication Date

9-2-2013

Publication Title

GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference

Number of Pages

215-222

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2463372.2463393

Socpus ID

84883106298 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84883106298

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