Genetic Algorithm Application to Constrained Optimization Problem
Dr. Elmer Dadios
De La Salle University
This paper presents new approach of Genetic algorithm (GA) to solve constrained optimization problem. In a constrained optimization problem, feasible and infeasible regions occupy the search space. The infeasible regions consist of the solutions that violate the constraint.
Oftentimes classical genetic operators generate infeasible of invalid chromosomes. This situation becomes worst when only infeasible chromosomes occupy the whole population.
To address this problem dynamic and adaptive penalty functions is proposed for the GA search process. This is a novel strategy because it will attempt to transform the constrained problem into unconstrained problem by penalizing the GA fitness function dynamically and adaptively.
New equations describing these functions are presented and tested. The effects of the proposed functions developed have been investigated and tested using different GA parameters such as mutation and crossover.
Comparisons of the performance of the proposed adaptive and dynamic penalty functions against traditional static penalty functions are presented. Result of the experiments show that the proposed functions developed is more accurate, efficient, robust and easy to implement.