Genetic algorithm with dynamic variable number of individuals and accuracy

Abstract

This paper proposes a novel processor for genetic algorithm (GA) that can dynamically change number of individuals and accuracy. In conventional GA, number of population and accuracy are fixed. However, the accuracy of solution is low at first-half stage. Therefore, the number of population is doubled at expense of the accuracy of solution, and the searching ability is improved at first-stage in the proposed GA processor. Then, the number of population is reduced by half, and the accuracy is improved at second-half stage. As a result, the searching ability is improved. The proposed GA processor was designed and verified. The effectiveness of proposed method was confirmed by applying to the knapsack problem.