Sustaining diversity using behavioral information distance
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Why and how to measure exploration in behavioral space
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
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Behavioral Diversity (BD) aims at improving the evolution of robots by fostering exploration on the basis of their behavior, whereas evolutionary algorithms typically consider the diversity on a genotypic level. Several Behavioral Similarity Measures (BSM), the key component to improve behavioral diversity, have been investigated in the litterature. Current benchmarks show that (1) most tested BSM improve the final performance, (2) they do not lead to the same improvements and, (3) it is hard to predict a priori which BSM will work the best. Instead of trying to find the best BSM, a different approach is proposed here: assuming that several BSM are available, we propose to randomly switch between then each K generations (e.g. K=20). This new approach is tested on a ball collecting task. Results show that better fitness values are obtained with the random switch approach than with any single measure. In effect, the present contribution show that it is possible use behavioral diversity while avoiding to choose between behavioral similarity measures.