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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)
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 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference(1)
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Ciencias de la computación(6)
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ODS 17: Alianzas para lograr los objetivos(10)
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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: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: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:scopusNew features for continuous exploratory landscape analysis based on the SOO tree
Conference ObjectAbstract: Extracting a priori knowledge informing about the landscape underlying an unknown optimization problPalabras claves:Blackbox landscape features, Exploratory landscape analysisAutores:Derbel B., Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S.Fuentes:scopusTowards landscape-aware automatic algorithm configuration: Preliminary experiments on neutral and rugged landscapes
Conference ObjectAbstract: The proper setting of algorithm parameters is a well-known issue that gave rise to recent research iPalabras claves:Autores:Derbel B., Hernán E. Aguirre, Liefooghe A., 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:scopusLandscape-Aware Performance Prediction for Evolutionary Multiobjective Optimization
ArticleAbstract: We expose and contrast the impact of landscape characteristics on the performance of search heuristiPalabras claves:Black-box combinatorial optimization, evolutionary multiobjective optimization (EMO), feature-based performance pbkp_rediction, problem difficulty and landscape analysisAutores:Daolio F., Derbel B., Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel 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:scopusLearning variable importance to guide recombination
Conference ObjectAbstract: In evolutionary multi-objective optimization, variation operators are crucially important to producePalabras claves:Autores:Daolio F., Derbel B., Hernán E. Aguirre, Liefooghe A., Sagawa M., Tanaka K., Verel S.Fuentes:scopus