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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)(3)
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 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference(1)
GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference(1)
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Ciencias de la computación(6)
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scopus(10)
Closed state model for understanding the dynamics of MOEAs
Conference ObjectAbstract: This work proposes the use of simple closed state models to capture, analyze and compare the dynamicPalabras 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:scopusAn analysis of differential evolution parameters on rotated bi-objective optimization functions
ArticleAbstract: Differential evolution (DE) is a very powerful and simple algorithm for single- and multi-objectivePalabras claves:Differential Evolution, multi-objective optimization, Parameter analysis, Rotational invarianceAutores:Derbel A.B., Drozdik M., 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: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: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: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