Varying quality function in genetic algorithms and the cutting problem
V. Petridis, S. Kazarlis · 2002
In this paper, an implementation of a genetic algorithm (GA) is presented, using a quality function that is not unaltered but changes according to the search evolution. This means that the GA 'sees' a continuously changing search space, throughout one run. The example chosen to test the effect of a varying quality function is the cutting problem. Simulation results show that the dynamic quality function performs much better than its static counterpart.>