Adaptive Parameterization of Evolutionary Algorithms Driven by Reproduction and Competition

running a genetic algorithm entails setting a number of parameter values. Finding settings that work well on one problem is not a trivial task and a genetic algorithm performance can be severely impacted. Moreover we know that in natural environments population sizes, reproduction and competition rates, change and tend to stabilise around appropriate values according to some environmental factors. This paper deals with a new technique for setting the genetic parameters during the course of a run by adapting the population size and the operators rates on the basis of the environmental constrain of maximum population size. In addition genetic operators are seen as alternative reproduction strategies and fighting among individuals is introduced. Finally benchmarks of the proposed strategy on classical optimization problems are shown. The results show that the parameters reach an equilibrium point and that performances on the considered problems are very good.