期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 22, 期 4, 页码 623-630出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2017.2735550
关键词
Behavioral diversity; centroidal Voronoi tessellation (CVT); illumination algorithms; multidimensional archive of phenotypic elites (MAP-Elites); quality diversity (QD)
资金
- European Research Council under the European Union's Horizon 2020 research and innovation programme (Project: ResiBots) [637972]
The recently introduced multidimensional archive of phenotypic elites (MAP-Elites) is an evolutionary algorithm capable of producing a large archive of diverse, high-performing solutions in a single run. It works by discretizing a continuous feature space into unique regions according to the desired discretization per dimension. While simple, this algorithm has a main drawback: it cannot scale to high-dimensional feature spaces since the number of regions increase exponentially with the number of dimensions. In this paper, we address this limitation by introducing a simple extension of MAP-Elites that has a constant, predefined number of regions irrespective of the dimensionality of the feature space. Our main insight is that methods from computational geometry could partition a high-dimensional space into well-spread geometric regions. In particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to divide the feature space into a desired number of regions; it then places every generated individual in its closest region, replacing a less fit one if the region is already occupied. We demonstrate the effectiveness of the new CVT-MAP-Elites algorithm in high-dimensional feature spaces through comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks.
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