A simple elitist genetic algorithm for constrained optimization

In this paper we propose a novel approach for solving constrained optimization problems using genetic algorithms. The main emphasis of this algorithm is to be problem independent and to produce consistent results in terms of the quality of feasible solutions. The basic characteristic of this algorithm is the complete ignorance of the objective function till at least one feasible solution is found. The elitist scheme is used to assure consistent results and to help guide the stochastic search to the more fruitful regions of the parameter space. We have used rank based fitness assignment and have experimented with two ranking schemes. We have developed an empirical analysis and supporting experimental comparisons to favor one ranking scheme over the other. Irrespective of the ranking scheme used, our algorithm has performed well providing at least one feasible solution for every run of the algorithm and producing results that are comparable to the best published before.