DocumentCode
2293756
Title
Mind-evolution-based machine learning and applications
Author
Chengyi, Sun ; Yan, Sun ; Keming, Xie
Author_Institution
Comput. Center, Taiyuan Univ. of Technol., China
Volume
1
fYear
2000
fDate
2000
Firstpage
112
Abstract
This paper describes the foundation of proposing MEBML (mind-evolution-based machine learning) that was recently presented. Then the paper analyses the performance mechanism of MEBML which is different from GA (genetic algorithm), and its characteristics. With its own distinctive mechanism MEBML improved the efficiency and convergence rate greatly compared with GA. The paper also summarizes the recent development of researches on MEBML, which includes several different strategies of similarity and dissimilarity, the proof of convergence of MEBML and its applications. In addition the authors discuss the future work of MEBML
Keywords
computational complexity; convergence; evolutionary computation; learning (artificial intelligence); MEBML; convergence rate; dissimilarity; mind-evolution-based machine learning; similarity; Algorithm design and analysis; Application software; Convergence; Educational institutions; Evolutionary computation; Genetic algorithms; Machine learning; Performance analysis; Sun;
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.2000.859927
Filename
859927
Link To Document