Open AccessA Machine-Learning Approach to Select Important Variables for Recombination on Many-objective Evolutionary Optimization
Author(s) -
Miyako Sagawa,
Hernán Aguirre,
Fabio Daolio,
Arnaud Liefooghe,
Bilel Derbel,
Sebástien Vérel,
Kiyoshi Tanaka
Publication year - 2018
Publication title -
international journal of smart computing and artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2185-9914
pISSN - 2185-9906
DOI - 10.52731/ijscai.v2.i1.285
Subject(s) - benchmark (surveying) , evolutionary algorithm , computer science , multi objective optimization , variation (astronomy) , variable (mathematics) , convergence (economics) , mathematical optimization , ranking (information retrieval) , selection (genetic algorithm) , pareto principle , optimization problem , artificial intelligence , machine learning , mathematics , mathematical analysis , physics , geodesy , economic growth , astrophysics , economics , geography