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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)
2013 IEEE Congress on Evolutionary Computation, CEC 2013(1)
2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016(1)
Evolutionary Computation(1)
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Ciencias de la computación(8)
Programación informática, programas, datos, seguridad(3)
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Optimización matemática(8)
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ODS 17: Alianzas para lograr los objetivos(12)
ODS 9: Industria, innovación e infraestructura(12)
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scopus(12)
A study on population size and selection lapse in many-objective optimization
Conference ObjectAbstract: In this work we study the effects of population size on selection and performance scalability of twoPalabras claves:Autores:Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S.Fuentes:scopusClosed 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:scopusProblem features versus algorithm performance on rugged multiobjective combinatorial fitness landscapes
ArticleAbstract: In this article, we attempt to understand and to contrast the impact of problem features on the perfPalabras claves:Black-box 0-1 multiobjective problems, Empirical performance modeling, Evolutionary multiobjective optimization, Feature-based analysis, Fitness landscape and problem difficulty, Multilevel multivariate analysis, Random-effects mixed modelsAutores:Daolio F., Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S.Fuentes:scopus