RT Conference Proceedings
T1 Combination of Evolutionary Algorithms with Experimental Design, Traditional Optimization and Machine Learning
A1 Zhang, Qingfu
K1 Computación evolutiva
AB 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 algorithmwhich 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.
YR 2016
FD 2016-05-24
LK http://hdl.handle.net/10630/11481
UL http://hdl.handle.net/10630/11481
LA eng
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026