Searching For Quality Diversity When Diversity Is Unaligned With Quality

Keywords

Behavioral diversity; Neuroevolution; Non-objective search; Novelty search; Quality diversity

Abstract

Inspired by natural evolution’s affinity for discovering a wide variety of successful organisms, a new evolutionary search paradigm has emerged wherein the goal is not to find the single best solution but rather to collect a diversity of unique phenotypes where each variant is as good as it can be. These quality diversity (QD) algorithms therefore must explore multiple promising niches simultaneously. A QD algorithm’s diversity component, formalized by specifying a behavior characterization (BC), not only generates diversity but also promotes quality by helping to overcome deception in the fitness landscape. However, some BCs (particularly those that are unaligned with the notion of quality) do not adequately mitigate deception, rendering QD algorithms unable to discover the best-performing solutions on difficult problems. This paper introduces a solution that enables QD algorithms to pursue arbitrary notions of diversity without compromising their ability to solve hard problems: driving search with multiple BCs simultaneously.

Publication Date

1-1-2016

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

9921 LNCS

Number of Pages

880-889

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-319-45823-6_82

Socpus ID

84988515819 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84988515819

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