Adaptive Parameterization of Evolutionary Algorithms and Chaotic Artificial Populations

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. The algorithm is a particular instance of a chaotic map similar to the logistic map