The analysis of searching efficiency of similartaxis
Chengyi Sun, Jianqing Zhang, Junli Wang · 2002
Mind Evolutionary Computation (MEC) is a new approach to evolutionary computation (EC). It is shown that MEC has a much higher computing efficiency and convergence ability than genetic algorithms (GA). A novel method of analyzing similartaxis process is presented. We obtained the relation between the calculated amount in similartaxis and the parameters of MEC, including the parameters of a probability density function for scattering individuals, the size of group, the precision of solution and the distance between initial searching position and local optimum with this method. Experimentation shows that the analysis result agrees with the experimental data and the analysis method is correct. The experiment also analyzes the influence of different sizes of groups on searching efficiency and a reasonable range of the size of groups is achieved. The analysis can also be used to direct the improvement of MEC performance. To sum up, this analyzing method is reasonable, feasible and directive.