An mind-evolution method for solving numerical optimization problems

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.