Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Luis Miguel Antonio"'
Autor:
Adriana Menchaca-Mendez, Elizabeth Montero, Luis Miguel Antonio, Saul Zapotecas-Martinez, Carlos A. Coello Coello, Maria-Cristina Riff
Publikováno v:
IEEE Access, Vol 7, Pp 18267-18283 (2019)
Convergence and diversity of solutions play an essential role in the design of multi-objective evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the $\epsilon $ -dominance has shown a proper balance between convergence and di
Externí odkaz:
https://doaj.org/article/ece314f911f2436d899969ed4f79b1c6
Autor:
Elizabeth Montero, Adriana Menchaca-Mendez, Carlos A. Coello Coello, Saúl Zapotecas-Martínez, María Cristina Riff, Luis Miguel Antonio
Publikováno v:
IEEE Access, Vol 7, Pp 18267-18283 (2019)
Convergence and diversity of solutions play an essential role in the design of multi-objective evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the $\epsilon $ -dominance has shown a proper balance between convergence and di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93b352c4799d17bf6aa38508dec292fc
https://ieeexplore.ieee.org/document/8637162/
https://ieeexplore.ieee.org/document/8637162/
Publikováno v:
IEEE Transactions on Evolutionary Computation. 22:851-865
In the last 20 years, evolutionary algorithms (EAs) have shown to be an effective method to solve multiobjective optimization problems (MOPs). Due to their population-based nature, multiobjective EAs (MOEAs) are able to generate a set of tradeoff sol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7babe2853efbcee75cb57fbcad684ed2
https://doi.org/10.1109/tevc.2017.2767023
https://doi.org/10.1109/tevc.2017.2767023
Publikováno v:
Engineering Applications of Artificial Intelligence. 111:104795
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5dc46fe39398cb1d5584798febb69187
https://doi.org/10.1016/j.engappai.2022.104795
https://doi.org/10.1016/j.engappai.2022.104795
Autor:
Luis Miguel Antonio, Silvia González Brambila, Mario A. Ramirez Morales, Josué Figueroa González, Carlos A. Coello Coello, Guadalupe Castillo Tapia
Publikováno v:
CEC
Multi-objective evolutionary algorithms (MOEAs) of the state of the art are created with the only purpose of dealing with the number of objective functions in a multi-objective optimization problem (MOP) and treat the decision variables of a MOP as a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1f5a94f6f47441a1b232047fbc56a3f1
https://doi.org/10.1109/cec48606.2020.9185846
https://doi.org/10.1109/cec48606.2020.9185846
Publikováno v:
Soft Computing. 22:5491-5512
The selection mechanisms that are most commonly adopted by multi-objective evolutionary algorithms (MOEAs) are based on Pareto optimality. However, recent studies have provided theoretical and experimental evidence regarding the unsuitability of Pare
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fa2bab247d6d5bf464aea3a4d578e8ae
https://doi.org/10.1007/s00500-018-3164-3
https://doi.org/10.1007/s00500-018-3164-3
Autor:
Guadalupe Castillo Tapia, Silvia González Brambila, Luis Miguel Antonio, Josué Figueroa González, Carlos A. Coello Coello
Publikováno v:
GECCO (Companion)
Most multi-objective evolutionary algorithms (MOEAs) of the state of the art treat the decision variables of a multi-objective optimization problem (MOP) as a whole. However, when dealing with MOPs with a large number of decision variables (more than
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1cfa846a6c99b2f74e900b2359020a78
https://doi.org/10.1145/3319619.3322068
https://doi.org/10.1145/3319619.3322068
Publikováno v:
CEC
Cooperative coevolutionary algorithms (CCAs) are extensions of traditional Evolutionary Algorithms (EAs) that have a lot of potential in addressing some problems on which EAs tend to perform poorly. CCAs have become an important area of research with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::94c7787b449268cdf03dc595ea0ff74c
https://doi.org/10.1109/cec.2016.7743897
https://doi.org/10.1109/cec.2016.7743897
Publikováno v:
Parallel Problem Solving from Nature – PPSN XIV ISBN: 9783319458229
PPSN
PPSN
Decomposition is a well-established mathematical programming technique for dealing with multi-objective optimization problems (MOPs), which has been found to be efficient and effective when coupled to evolutionary algorithms, as evidenced by MOEA/D.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::53b51733e9f582c47d3441cd429d5dd9
https://doi.org/10.1007/978-3-319-45823-6_49
https://doi.org/10.1007/978-3-319-45823-6_49
