Self-Referential Quality Diversity Through Differential Map-Elites
This work addresses the need for more sophisticated optimizers in evolutionary computation, particularly for quality-diversity algorithms, offering incremental improvements for researchers in this domain.
The paper tackled the problem of improving quality-diversity algorithms by combining Differential Evolution with CVT-MAP-Elites, resulting in Differential MAP-Elites, which outperformed CVT-MAP-Elites by finding better-quality and more diverse solutions in experiments on 25 numerical optimization problems.
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.