The influence of the probability density function on similartaxis in MEC

Mind evolutionary computation (MEC) is a new approach of evolutionary computation (EC). It is proved that MEC has much higher computing efficiency and convergence ability than genetic algorithms (GAs). This is because of using operation similartaxis and dissimilation rather than crossover and mutation operators in GA. The paper analyzes the influence of type of the probability density function on similartaxis in MEC. We get theoretically the relation among similartaxis calculated amount, the parameters of probability density function of scattering individuals, the size of group, the precision of solution and the distance between initial searching position and local optimum. The experiment shows that the analysis method proposed in the paper is reasonable. The analysis and experiment also shows that using different types of probability density functions doesn´t make much change on similartaxis searching performance