Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/22969
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dc.contributor.authorGravina, Daniele-
dc.contributor.authorLiapis, Antonios-
dc.contributor.authorYannakakis, Georgios N.-
dc.date.accessioned2017-10-24T10:13:43Z-
dc.date.available2017-10-24T10:13:43Z-
dc.date.issued2017-
dc.identifier.citationGravina, D., Liapis, A., & Yannakakis G. N. (2017). Exploring divergence in soft robot evolution. Genetic and Evolutionary Computation Conference, Berlin. 61-62.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar//handle/123456789/22969-
dc.description.abstractDivergent 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.en_GB
dc.language.isoenen_GB
dc.publisherACM Publicationsen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectArtificial lifeen_GB
dc.subjectGenetic algorithmsen_GB
dc.subjectEvolutionary roboticsen_GB
dc.titleExploring divergence in soft robot evolutionen_GB
dc.typeconferenceObjecten_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.bibliographicCitation.conferencenameGenetic and evolutionary computation conferenceen_GB
dc.bibliographicCitation.conferenceplaceBerlin, Germany, 15-19/07/2017en_GB
dc.description.reviewedpeer-revieweden_GB
dc.identifier.doi10.1145/3067695.3076072-
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