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dc.contributor.authorFaris, Hossam
dc.contributor.authorAljarah, Ibrahim
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorCastillo, Pedro A
dc.contributor.authorMerelo, Juan J
dc.contributor.editorMerelo, JJ
dc.contributor.editorMelicio, F
dc.contributor.editorCadenas, JM
dc.contributor.editorDourado, A
dc.contributor.editorMadani, K
dc.contributor.editorRuano, A
dc.contributor.editorFilipe, J
dc.date.accessioned2021-01-18T01:49:19Z
dc.date.available2021-01-18T01:49:19Z
dc.date.issued2016
dc.identifier.isbn9789897582011
dc.identifier.doi10.5220/0006048201710177
dc.identifier.urihttp://hdl.handle.net/10072/401215
dc.description.abstractEvoloPy is an open source and cross-platform Python framework that implements a wide range of classical and recent nature-inspired metaheuristic algorithms. The goal of this framework is to facilitate the use of metaheuristic algorithms by non-specialists coming from different domains. With a simple interface and minimal dependencies, it is easier for researchers and practitioners to utilize EvoloPy for optimizing and benchmarking their own defined problems using the most powerful metaheuristic optimizers in the literature. This framework facilitates designing new algorithms or improving, hybridizing and analyzing the current ones.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherSCITEPRESS
dc.relation.ispartofconferencename8th International Joint Conference on Computational Intelligence
dc.relation.ispartofconferencetitleProceedings of the 8th International Joint Conference on Computational Intelligence
dc.relation.ispartofdatefrom2016-11-09
dc.relation.ispartofdateto2016-11-11
dc.relation.ispartoflocationPorto, PORTUGAL
dc.relation.ispartofpagefrom171
dc.relation.ispartofpageto177
dc.relation.ispartofvolume1
dc.subject.fieldofresearchSoftware engineering
dc.subject.fieldofresearchcode4612
dc.subject.keywordsScience & Technology
dc.subject.keywordsComputer Science, Interdisciplinary Applications
dc.subject.keywordsEvolutionary
dc.titleEvoloPy: An Open-source Nature-inspired Optimization Framework in Python
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationFaris, H; Aljarah, I; Mirjalili, S; Castillo, PA; Merelo, JJ, EvoloPy: An Open-source Nature-inspired Optimization Framework in Python, Proceedings of the 8th International Joint Conference on Computational Intelligence, 2016, 1, pp. 171-177
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2021-01-18T01:37:25Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) License, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


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