2026-06-01T09:17:34Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/114812026-02-03T12:15:36Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Zhang, Qingfu author 2016-05-24 Evolutionary algorithms alone cannot solve optimization problems very efficiently since there are many random (not very rational) decisions in these algorithms. Combination of evolutionary algorithms and other techniques have been proven to be an efficient optimization methodology. In this talk, I will explain the basic ideas of our three algorithms along this line (1): Orthogonal genetic algorithm which treats crossover/mutation as an experimental design problem, (2) Multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses decomposition techniques from traditional mathematical programming in multiobjective optimization evolutionary algorithm, and (3) Regular model based multiobjective estimation of distribution algorithms (RM-MEDA) which uses the regular property and machine learning methods for improving multiobjective evolutionary algorithms. http://hdl.handle.net/10630/11481 Computación evolutiva Combination of Evolutionary Algorithms with Experimental Design, Traditional Optimization and Machine Learning