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Title: Blending notions of diversity for MAP-Elites
Authors: Gravina, Daniele
Liapis, Antonios
Yannakakis, Georgios N.
Keywords: Robotics
Evolutionary robotics
Genetic programming (Computer science)
Issue Date: 2019
Publisher: Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation
Citation: Gravina, D., Liapis, A., & Yannakakis, G. N. (2019). Blending notions of diversity for MAP-Elites. Proceedings of the Genetic and Evolutionary Computation Conference, Prague.
Abstract: Quality-diversity algorithms focus on discovering multiple diverse and high-performing solutions. MAP-elites is such an algorithm, as it partitions the solution space into bins and searches for the best solution possible for each bin. In this paper, multi-behavior variants of MAP-Elites are tested where the MAP-Elites grid partitions the solution space based on a certain dimension, while selection is guided by measures of diversity on another dimension. Four divergent search algorithms are tested for this selection process, targeting novelty or surprise or their combination, and their performance on a soft robot evolution task is discussed.
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