Self-Referential Quality Diversity Through Differential Map-Elites

TL;DR

This paper introduces Differential MAP-Elites, a new algorithm that merges the strengths of CVT-MAP-Elites and Differential Evolution, demonstrating superior performance in diverse numerical optimization tasks.

Contribution

It presents the first implementation of Differential MAP-Elites, combining illumination and continuous optimization, and shows its effectiveness over existing methods.

Findings

Differential MAP-Elites outperforms CVT-MAP-Elites in quality and diversity.

The new algorithm effectively combines map-based illumination with differential evolution.

Experimental results on 25 problems validate its superior optimization capabilities.

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.