Mostrando 7 resultados de: 7
Publisher
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)(2)
Advances in Operations Research(1)
GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference(1)
GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion(1)
GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion(1)
Área de conocimiento
Optimización matemática(4)
Algoritmo(3)
Control óptimo(1)
Evolución(1)
Modelo matemático(1)
Origen
scopus(7)
A feature-based performance analysis in evolutionary multiobjective optimization
Conference ObjectAbstract: This paper fundamentally investigates the performance of evolutionary multiobjective optimization (EPalabras claves:Autores:Daolio F., Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S.Fuentes:scopusA set-oriented MOEA/D
Conference ObjectAbstract: The working principles of the well-established multi-objective evolutionary algorithm Moea/d reliesPalabras claves:Combinatorial optimization, decomposition, Evolutionary algorithms, many-objective optimization, Multi-Autores:Derbel B., Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S., Zhang Q.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:scopusPareto dominance-based MOEAs on problems with difficult pareto set topologies
Conference ObjectAbstract: Despite the extensive application of multi-objective evolutionary algorithms (MOEAs) to solve multi-Palabras claves:Differential Evolution, multi-objective optimization, Recombination operators, Selection, Working principles of evolutionary computingAutores:Derbel B., Hernán E. Aguirre, Liefooghe A., Marca Y., Tanaka K., Verel S., Zapotecas-Martínez S.Fuentes:scopusLearning Variable Importance to Guide Recombination on Many-Objective Optimization
Conference ObjectAbstract: There are numerous many-objective real-world problems in various application domains for which it isPalabras claves:evolutionary algorithm, Machine learning, many-objective optimization, multi-objective optimization, random forest, variable importanceAutores:Daolio F., Derbel B., Hernán E. Aguirre, Liefooghe A., Sagawa M., Tanaka K., Verel S.Fuentes:scopusUnderstanding Population Dynamics in Multi- and Many-Objective Evolutionary Algorithms for High-Resolution Approximations
ArticleAbstract: Achieving a high-resolution approximation and hitting the Pareto optimal set with some if not all mePalabras claves:Autores:Derbel B., Hernán E. Aguirre, Liefooghe A., Monzón Maldonado H., Tanaka K., Verel S.Fuentes:scopusWhat makes an instance difficult for black-box 0–1 evolutionary multiobjective optimizers?
Conference ObjectAbstract: This paper investigates the correlation between the characteristics extracted from the problem instaPalabras claves:Autores:Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S.Fuentes:scopus