Varying Fitness Functions in Genetic Algorithms: Studying the Rate of Increase of the Dynamic Penalty Terms

In this paper we present a promising technique that enhances the efficiency of GAs, when they are applied to constrained optimisation problems. According to this technique, the problem constraints are included in the fitness function as penalty terms, that vary during the GA evolution, facilitating thus the location of the global optimum and the avoidance of local optima. Moreover we proceed to test the effect that the rate of change in the fitness function has on GA performance. The tests are performed on two well-known real-world optimisation problems : the Cutting Stock problem and the Unit Commitment problem. Comparative results are reported.