Loading [MathJax]/extensions/MathMenu.js
An mind-evolution method for solving numerical optimization problems | IEEE Conference Publication | IEEE Xplore

An mind-evolution method for solving numerical optimization problems


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 a...Show More

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.
Date of Conference: 26 June 2000 - 02 July 2000
Date Added to IEEE Xplore: 06 August 2002
Print ISBN:0-7803-5995-X
Conference Location: Hefei

Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.


Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.

Contact IEEE to Subscribe

References

References is not available for this document.