Mostrando 10 resultados de: 16
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)(9)
2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016(1)
FOGA 2019 - Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms(1)
GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion(1)
GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference(1)
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Análisis(1)
Ciencias Naturales y Matemáticas(1)
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Optimización matemática(12)
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scopus(16)
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:scopusDecomposition-Based Multi-objective Landscape Features and Automated Algorithm Selection
Conference ObjectAbstract: Landscape analysis is of fundamental interest for improving our understanding on the behavior of evoPalabras claves:Autores:Cosson R., Derbel B., Hernán E. Aguirre, Liefooghe A., Tanaka K., Zhang Q.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: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:scopusStudying MOEAs dynamics and their performance using a three compartmental model
Conference ObjectAbstract: The road to a better design of multi- and many-objective evolutionary algorithms requires a deeper uPalabras claves:empirical study, Genetic Algorithms, multi-objective optimization, Working principles of evolutionary computingAutores:Derbel B., Hernán E. Aguirre, Liefooghe A., Monzón H., Tanaka K., Verel S.Fuentes:scopusStudying compartmental models interpolation to estimate MOEAS population size
Conference ObjectAbstract: Dynamical compartmental models capture the population dynamics of Multi-objective Optimization EvoluPalabras claves:empirical study, Genetic Algorithms, multi-objective optimization, Working principles of evolutionary computingAutores:Derbel B., Hernán E. Aguirre, Liefooghe A., Monzón H., Tanaka K., Verel S.Fuentes:scopus