Performance improvement of self-adaptive evolutionary methods with a dynamic lower bound

Abstract

Recent research on self-adaptive evolutionary programming (EP) methods evidenced the problem of premature convergence. Self-adaptive evolutionary programming methods converge prematurely because their object variables evolve more slowly than do their strategy parameters, which subsequently leads to a stagnation of object variables at a non-optimum value. To address this problem, a dynamic lower bound has been proposed, which is defined here as the differential step lower bound (DSLB) on the strategy parameters. The DSLB on an object variable depends on its absolute distance from the corresponding object variable of the best individual in the population pool. The performance of the self-adaptive EP algorithm with DSLB has been verified over eight different test functions of varied complexities.