Discovering the Elite Hypervolume by Leveraging Interspecies Correlation

Vassilis Vassiliades 1 Jean-Baptiste Mouret 1
1 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
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).
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Vassilis Vassiliades, Jean-Baptiste Mouret. Discovering the Elite Hypervolume by Leveraging Interspecies Correlation. GECCO 2018 - Genetic and Evolutionary Computation Conference, Jul 2018, Kyoto, Japan. ⟨10.1145/3205455.3205602⟩. ⟨hal-01764739⟩

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