Self-Referential Quality Diversity Through Differential Map-Elites

• URL: http://arxiv.org/abs/2107.04964v1
• Date: Sun, 11 Jul 2021 04:31:10 GMT
• Title: Self-Referential Quality Diversity Through Differential Map-Elites
• Authors: Tae Jong Choi and Julian Togelius
• Abstract summary: Differential MAP-Elites is a novel algorithm that combines the illumination capacity of computation-MAP-Elites with the continuous-space optimization capacity of Differential Evolution. The basic MAP-Elites algorithm, introduced for the first time here, is relatively simple in that it simply combines the operators from Differential Evolution with the map structure of Differential-MAP-Elites.
• Score: 5.2508303190856624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differential MAP-Elites is a novel algorithm that combines the illumination capacity of CVT-MAP-Elites with the continuous-space optimization capacity of Differential Evolution. The algorithm is motivated by observations that illumination algorithms, and quality-diversity algorithms in general, offer qualitatively new capabilities and applications for evolutionary computation yet are in their original versions relatively unsophisticated optimizers. The basic Differential MAP-Elites algorithm, introduced for the first time here, is relatively simple in that it simply combines the operators from Differential Evolution with the map structure of CVT-MAP-Elites. Experiments based on 25 numerical optimization problems suggest that Differential MAP-Elites clearly outperforms CVT-MAP-Elites, finding better-quality and more diverse solutions.

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