Mostrando 10 resultados de: 17
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)(8)
GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion(2)
GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion(2)
GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference(1)
GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference(1)
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Ciencias de la computación(12)
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scopus(17)
A fitness landscape analysis of Pareto local search on bi-objective permutation flowshop scheduling problems
Conference ObjectAbstract: We study the difficulty of solving different bi-objective formulations of the permutation flowshop sPalabras claves:Autores:Derbel B., Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S.Fuentes:scopusA surrogate model based on walsh decomposition for pseudo-boolean functions
Conference ObjectAbstract: Extensive efforts so far have been devoted to the design of effective surrogate models aiming at redPalabras claves:Autores:Derbel B., Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S.Fuentes:scopusCost-vs-accuracy of sampling in multi-objective combinatorial exploratory landscape analysis
Conference ObjectAbstract: The design of effective features enabling the development of automated landscape-aware techniques rePalabras claves:automated algorithm selection, landscape analysis, multi-objective optimization, NK-landscapesAutores:Cosson R., Derbel B., Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S., Zhang Q.Fuentes:scopusApproximating pareto set topology by cubic interpolation on bi-objective problems
Conference ObjectAbstract: Difficult Pareto set topology refers to multi-objective problems with geometries of the Pareto set sPalabras claves:Difficult Pareto set topology, evolutionary algorithm, Interpolation, multi-objective optimizationAutores:Derbel B., Hernán E. Aguirre, Liefooghe A., Marca Y., Tanaka K., Verel S., Zapotecas-Martínez S.Fuentes:scopusDesigning parallelism in surrogate-assisted multiobjective optimization based on decomposition
Conference ObjectAbstract: On the one hand, surrogate-assisted evolutionary algorithms are established as a method of choice foPalabras claves:Multiobjective optimization, Parallelism benchmarking, SurrogatesAutores:Berveglieri N., Derbel B., Hernán E. Aguirre, Liefooghe A., Tanaka K., Zhang Q.Fuentes:scopusGeometric differential evolution in MOEA/D: A preliminary study
Conference ObjectAbstract: The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an aggregation-based aPalabras claves:Autores:Derbel B., Hernán E. Aguirre, Liefooghe A., Tanaka K., Zapotecas-Martínez S.Fuentes:scopusEstimating relevance of variables for effective recombination
Conference ObjectAbstract: Dominance, extensions of dominance, decomposition, and indicator functions are well-known approachesPalabras claves:Evolutionary multi-objective optimization, Recombination operators, Variables classification, Variables selectionAutores:Derbel B., Hernán E. Aguirre, Ito T., Liefooghe A., Tanaka K., Verel S.Fuentes:scopusDominance, indicator and decomposition based search for multi-objective qap: Landscape analysis and automated algorithm selection
Conference ObjectAbstract: We investigate the properties of large-scale multi-objective quadratic assignment problems (mQAP) anPalabras claves:Autores:Derbel B., Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S.Fuentes:scopusDynamic Compartmental Models for Large Multi-objective Landscapes and Performance Estimation
Conference ObjectAbstract: Dynamic Compartmental Models are linear models inspired by epidemiology models to study Multi- and MPalabras claves:compartmental models, Hypervolume estimation, Modeling, multi-objective optimization, Population dynamicsAutores:Derbel B., Hernán E. Aguirre, Liefooghe A., Monzón H., Tanaka K., Verel S.Fuentes:scopusDynamic compartmental models for algorithm analysis and population size estimation
Conference ObjectAbstract: Dynamic Compartmental Models (DCM) can be used to study the population dynamics of Multi- and Many-oPalabras claves:compartmental models, empirical study, Genetic Algorithms, Modeling, multi-objective optimization, Working principles of evolutionary computingAutores:Derbel B., Hernán E. Aguirre, Liefooghe A., Monzón H., Tanaka K., Verel S.Fuentes:scopus