Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
This work addresses the efficiency of evolutionary algorithms for generating diverse solutions in robotics and optimization, representing an incremental improvement over existing methods.
The paper tackles the problem of accelerating the MAP-Elites algorithm for finding diverse, high-performing solutions by introducing the concept of an 'elite hypervolume' and a 'directional variation' operator that exploits correlations between elites. It demonstrates effectiveness in three problems, including a hexapod robot, though no concrete performance numbers are provided in the abstract.
Evolution has produced an astonishing diversity of species, each filling a different niche. Algorithms like MAP-Elites mimic this divergent evolutionary process to find a set of behaviorally diverse but high-performing solutions, called the elites. Our key insight is that species in nature often share a surprisingly large part of their genome, in spite of occupying very different niches; similarly, the elites are likely to be concentrated in a specific "elite hypervolume" whose shape is defined by their common features. In this paper, we first introduce the elite hypervolume concept and propose two metrics to characterize it: the genotypic spread and the genotypic similarity. We then introduce a new variation operator, called "directional variation", that exploits interspecies (or inter-elites) correlations to accelerate the MAP-Elites algorithm. We demonstrate the effectiveness of this operator in three problems (a toy function, a redundant robotic arm, and a hexapod robot).