A STUDY OF CONVERGENCE OF MIND EVOLUTION BASED MACHINE LEARNING

Based on the analysis of Markov chain on mind evolution based machine learning（MEBML）,it is proved that the population generated by the similartaxis operation converges to the global optimum with probability 1 in discrete space.But because of the local property of the similartaxis operation, the transition probability from a local optimum to the global optimum is very small.To increase this transition probability,the dissimilation operation is introduced.Moreover,with the concepts of P optimal state and convergent region,theoretical and practical values of similartaxis and dissimilation operations are analyzed.