An evolutionary and cooperative agents model for optimization

The authors propose the use of genetic algorithms (GA) to optimize another algorithm for optimization. The aim is to integrate the approach introduced by Dorigo et al., known as the ant system, with GA, exploiting the cooperative effect of the latter and the evolutionary effect of GA. An ant algorithm aims to solve problems of combinatorial optimization by means of a population of agents/processors that work parallel without a supervisor in a cooperative manner. A genetic algorithm aims to optimize the performance of the ant population by selecting optimal values for its parameters by means of evolution of the genetic patrimony associated with each single agent. The approach has been applied to the traveling salesman problem; results and comparisons with the original method are presented