Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/22969
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. |
URI: | https://www.um.edu.mt/library/oar//handle/123456789/22969 |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
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surprise_softrobots.pdf Restricted Access | 612.5 kB | Adobe PDF | View/Open Request a copy |
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