Behavior-based speciation for evolutionary robotics

Published in Conferences Papers
  1. Leonardo Trujillo, Gustavo Olague, Evelyne Lutton and Francisco Fernández Vega. Behavior-based speciation for evolutionary robotics. In GECCO. 2008, 297-298. BibTeX

    @inproceedings{DBLP:conf/gecco/TrujilloOLV08,
    	author = "Leonardo Trujillo and Gustavo Olague and Evelyne Lutton and Francisco Fern{\'a}ndez de Vega",
    	title = "Behavior-based speciation for evolutionary robotics",
    	booktitle = "GECCO",
    	year = 2008,
    	pages = "297-298",
    	ee = "http://doi.acm.org/10.1145/1389095.1389147",
    	crossref = "DBLP:conf/gecco/2008",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
  2. Conor Ryan and Maarten Keijzer (eds.). Genetic and Evolutionary Computation Conference, GECCO 2008, Proceedings, Atlanta, GA, USA, July 12-16, 2008. ACM, 2008. BibTeX

    @proceedings{DBLP:conf/gecco/2008,
    	editor = "Conor Ryan and Maarten Keijzer",
    	title = "Genetic and Evolutionary Computation Conference, GECCO 2008, Proceedings, Atlanta, GA, USA, July 12-16, 2008",
    	booktitle = "GECCO",
    	publisher = "ACM",
    	year = 2008,
    	isbn = "978-1-60558-130-9",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    
Abstract

This paper describes a speciation method that allows an evolutionary process to learn several robot behaviors using a single execution. Species are created in behavioral space in order to promote the discovery of different strategies that can solve the same navigation problem. Candidate neurocontrollers are grouped into species based on their corresponding behavior signature, which represents the traversed path of the robot within the environment.Behavior signatures are encoded using character strings and are compared using the string edit distance. The proposed approach is better suited for an evolutionary robotics problem than speciating in objective or topological space. Experimental comparison with the NEAT method confirms the usefulness of the proposal.

Published in
GECCO '08 Proceedings of the 10th annual conference on Genetic and evolutionary computation
Pages 297-298
http://dl.acm.org/citation.cfm?id=1389147
Date of conference
12 - 18 July 2008
ISBN
978-1-60558-130-9
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