Mind-evolution-based machine learning: an efficient approach of evolution computation
Chengyi Sun, Yan Sun, Xie Keming · 2002
This paper analyses mind-evolution-based machine learning (MEBML) that has been proposed recently. The paper first discusses the practical problems in the implement of MEBML in numerical optimization. Then the paper gives the criterion used to judge whether a group is mature, also the paper proposes a adaptive method to adjust the parameters in similar taxis. The results of the experiment of numerical optimization are given. The experiment shows that the global convergence rate and computation efficiency are both improved above 20% compared with standard genetic algorithm. The improvement in convergence rate and efficiency is due to the distinctive structure of MEBML and the introduction of similar taxis and dissimilation.