Exploring divergence in soft robot evolution

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.