Convergence Analysis of Mind Evolutionary Algorithm Based on Functional Analysis

As one of new type evolutionary computing methods, mind evolutionary algorithm processes evolutionary operations by using 'similartax' and 'dissimilation' operator. This paper studies the variance of population during the evolution from the view of the functional analysis. Interval sheath theorem is used to prove the global convergence of the algorithm. The conclusion is validated again by the numerical experiment results