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|Title:||Exploring divergence in soft robot evolution|
Yannakakis, Georgios N.
|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.|
|Appears in Collections:||Scholarly Works - InsDG|
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