DocumentCode
352660
Title
Research of the dissimilation strategy for MEBML
Author
Jianchao, Zeng ; Kai, Zha
Author_Institution
Div. of Syst. Simulation & Comput. Appl., Taiyuan Heavy Machine Inst., China
Volume
1
fYear
2000
fDate
2000
Firstpage
129
Abstract
MEBML, mind-evolution-based machine learning mainly consists of similar taxis and dissimilation operators. Especially, the dissimilation strategy has important effects on the evolution efficiency and global optimality. In the paper, five dissimilation strategies based on the analysis of effects of the dissimilation operator in MEBML and the dissimilation mechanism. Finally, comparisons of these dissimilation strategies are made through the example of a global optimization problem
Keywords
evolutionary computation; learning (artificial intelligence); optimisation; MEBML; dissimilation operator; dissimilation strategies; dissimilation strategy; evolution efficiency; global optimality; global optimization problem; mind-evolution-based machine learning; similar taxis operator; Computational modeling; Computer applications; Computer simulation; Evolutionary computation; Machine learning; Machinery;
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.2000.859931
Filename
859931
Link To Document