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

1. INTRODUCTION

As a new type of evolutionary algorithm, Mind Evolutionary Algorithm (MEA) simulates the evolutionary process of human being's mind. It extracts the virtues from GA and ES and overcomes their disadvantages. ‘Similartax’ and ‘dissimilation’ operators are presented and monolayer population evolution is improved to multilayer population evolution. Since memory and directional learning mechanism are introduced, search efficiency has been increased greatly. Now, MEA has been successfully applied to solve some practical problems [2], [3].