Ensemble strategies with evolutionary programming and differential evolution for solving single objective optimization problems

Abstract

Evolutionary Algorithms (EAs) are population based algorithms that can tackle complex optimization problems with minimal information about the characteristics of the problem. The performance of Evolutionary Programming (EP), a veteran of the evolutionary computation community depends mostly on the mutation operation, where an offspring is produced from the parent by adding a scaled random number distribution. In EP, the scale factor is referred to as the strategy parameter and is self-adapted using a lognormal adaptation. The abrupt reduction in the strategy parameter values due to the lognormal self-adaptation may result in the premature convergence of the search process. To overcome the drawbacks of lognormal self-adaptation, we propose an adaptive EP (AEP). AEP is different from EP in terms of initialization and adaptation of the strategy parameter values. The parameters are initialized scaled to the search range and are adapted based on the search performance in the previous few generations.