DocumentCode
352659
Title
An mind-evolution method for solving numerical optimization problems
Author
Jianchao, Zeng ; Kai, Zha
Author_Institution
Div. of Syst. Simulation & Comput. Appl., Taiyuan Heavy Machinery Inst., China
Volume
1
fYear
2000
fDate
2000
Firstpage
126
Abstract
MEBML, mind-evolution-based machine learning presented in Chengyi Sun et al. (1998) has many superior qualities for solving the premature convergence problem of genetic algorithms and non-numerical optimization. The similar taxis and dissimilation operators have some shortcomings and no theoretical analysis method, so that the efficiency is lower. For numerical optimization problems, the construction methods of the similartaxis and dissimilation operators are given in the paper, and the effectiveness is proven through examples
Keywords
genetic algorithms; learning (artificial intelligence); optimisation; dissimilation operator; mind-evolution-based machine learning; numerical optimization problems; premature convergence problem; similar taxis operator; Computational modeling; Computer applications; Computer simulation; Convergence of numerical methods; Genetic algorithms; Machine learning; Machinery; Optimization methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location
Hefei
Print_ISBN
0-7803-5995-X
Type
conf
DOI
10.1109/WCICA.2000.859930
Filename
859930
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