Mostrando 5 resultados de: 5
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Conference Object(5)
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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 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion(1)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)(1)
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Ciencias de la computación(3)
Ciencias Naturales y Matemáticas(1)
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Ingeniería y operaciones afines(1)
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scopus(5)
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 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: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