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Title: Exploring divergence in soft robot evolution
Authors: Gravina, Daniele
Liapis, Antonios
Yannakakis, Georgios N.
Keywords: Artificial life
Genetic algorithms
Evolutionary robotics
Issue Date: 2017
Publisher: ACM Publications
Citation: Gravina, D., Liapis, A., & Yannakakis G. N. (2017). Exploring divergence in soft robot evolution. Genetic and Evolutionary Computation Conference, Berlin. 61-62.
Abstract: Divergent search is a recent trend in evolutionary computation that does not reward proximity to the objective of the problem it tries to solve. Traditional evolutionary algorithms tend to converge to a single good solution, using a fitness proportional to the quality of the problem's solution, while divergent algorithms aim to counter convergence by avoiding selection pressure towards the ultimate objective. This paper explores how a recent divergent algorithm, surprise search, can affect the evolution of soft robot morphologies, comparing the performance and the structure of the evolved robots.
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