A novel approach in parameter adaptation and diversity maintenance for genetic algorithms

Abstract

In this paper, we propose a probabilistic rule-driven adaptive model (PRAM) for parameter adaptation and a repelling approach for diversity maintenance in genetic algorithms. PRAM uses three parameter values and a set of greedy rules to adapt the value of the control parameters automatically. The repelling algorithm is proposed to maintain the population diversity. It modifies the fitness value to increase the survival opportunity of chromosomes with rare alleles. The computation overheads of repelling are reduced by the lazy repelling algorithm, which decreases the frequency of the diversity fitness evaluations. From experiments with commonly used benchmark functions, it is found that the PRAM and repelling techniques outperform other approaches on both solution quality and efficiency.