Convergence of MEC in bounded and continuous search space

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.