Justin K. Pugh, L. B. Soros, and Kenneth O. Stanley (2016)
Searching for Quality Diversity when Diversity is Unaligned with Quality
In: Proceedings of the 14th International Conference on Parallel Problem Solving From Nature (PPSN-2016). New York, NY: Springer (10 pages).
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.