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2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016(1)
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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:scopusGlobal vs local search on multi-objective NK-landscapes: Contrasting the impact of problem features
Conference ObjectAbstract: Computationally hard multi-objective combinatorial optimization problems are common in practice, andPalabras claves:Autores:Daolio F., Hernán E. Aguirre, Liefooghe A., Tanaka K., Verel S.Fuentes:scopusOn the effectiveness of feature selection methods for gait classification under different covariate factors
ArticleAbstract: Gait classification is the problem of recognising individuals by the way in which they walk. The prePalabras claves:Analysis of Variance (ANOVA), Biometrics, feature selection, gait recognition, metaheuristics, Mixed-effects linear modelsAutores:Daolio F., Hernán E. Aguirre, Tanaka K., Yeoh T.W.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: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