Convergence of a New Evolutionary Computing Algorithm in Continuous State Space

Abstract

A Markov chain on a new evolutionary computing algorithm is analyzed in continuous state space. By establishing transition probability density, the convergence of the similartaxis operator is proved. Meanwhile, the local property of the similartaxis operator is shown. To avoid its prematurity, a dissimilation operator need to be introduced. With the concept of P-absorbing field and P-optimal state, the convergence of the dissimilation operator is proved. We apply this new algorithm to a difficult problem for the accurate mixture ratio of raw materials of cement processing and make a comparison between GAs and the new algorithm. Finally, the functions of similartaxis and dissimilation operators are analyzed in a practical view.