Convergence of MEC in bounded and continuous search space

Author

Zhou, Xiuling ; Sun, Chengyi

Author_Institution

AI Inst., Beijing City Coll., China

Volume

2

fYear

2004

fDate

10-13 Oct. 2004

Firstpage

1412

Abstract

Mind evolutionary computation (MEC) is a new approach of evolutionary computation (EC) proposed by Chengyi Sun in 1998. The proposal is based on the study of the problems of GA and on the analysis of human mind progress. MEC comprises two kinds of operations - similartaxis and dissimilation. In this paper the operations similartaxis and dissimilation are theoretically described in detail. The wrong theorem in Chuan-long Wang and Ke-ming Xie, (2002) is pointed out in this paper. It is proved that the scatter center sequence of each group generated through similartaxis iteration converges in probability to local optimal state set. Estimation of upper bound of the convergence rate is given. It is finally proved that the sequence of mature groups generated through operations similartaxis and dissimilation converges in probability to global optimal set.

Keywords

convergence; genetic algorithms; probability; MEC convergence; bounded search space; continuous search space; human mind progress; mind evolutionary computation; optimal state set; scatter center sequence; similartaxis iteration; Artificial intelligence; Cities and towns; Convergence; Educational institutions; Evolutionary computation; Genetic algorithms; Genetic programming; Humans; Scattering; Sun;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2004 IEEE International Conference on

ISSN

1062-922X

Print_ISBN

0-7803-8566-7

Type

conf

DOI

10.1109/ICSMC.2004.1399828

Filename

1399828