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
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;
Conference_Titel :
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location :
Hefei
Print_ISBN :
0-7803-5995-X
DOI :
10.1109/WCICA.2000.859930
