Exploring divergence in soft robot evolution
GECCO (Companion)(2017)
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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Key words
Surprise search, novelty search, divergent search, deception, fitness based evolution, soft robots, CPPN, artificial life
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