An Extended Study Of Quality Diversity Algorithms
Keywords
Behavioral diversity; Neuroevolution; Non-objective search; Novelty search; Quality diversity
Abstract
In a departure from conventional optimization where the goal is to find the best possible solution, a new class of evolutionary algorithms instead search for quality diversity (QD) - a maximally diverse collection of individuals in which each member is as high-performing as possible. In QD, diversity of behaviors or phenotypes is defined by a behavior characterization (BC) that is typically unaligned with (i.e. orthogonal to) the notion of quality. As experiments in a difficult maze task reinforce, QD algorithms driven by such an unaligned BC are unable to discover the best solutions on sufficiently deceptive problems. This study comprehensively surveys known QD algorithms and introduces several novel variants thereof, including a method for successfully confronting deceptive QD landscapes: driving search with multiple BCs simultaneously.
Publication Date
7-20-2016
Publication Title
GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
Number of Pages
19-20
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1145/2908961.2909000
Copyright Status
Unknown
Socpus ID
84986325851 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84986325851
STARS Citation
Pugh, Justin K.; Soros, L. B.; and Stanley, Kenneth O., "An Extended Study Of Quality Diversity Algorithms" (2016). Scopus Export 2015-2019. 4316.
https://stars.library.ucf.edu/scopus2015/4316