Contrasting termination criteria for genetic algorithms

Abstract

To find a good termination criterion for genetic algorithms is a difficult and frequently ignored task. In most instances the practitioner stops the algorittm after a predefined number of generations or function evaluations. How this number is established? This stop criteria assume a user's knowledge on the characteristic of the function, which influence the length of the search. But usually it is difficult to say a priori that the total number of generations should be a detemined one. ConsequentIy this approach can involve a waste of computational resources, because the genetic algorithm could stagnate at some local or global optimum and no further improvement is achieved in that condition. This presentation discusses perfomance results on evolutionary algorithms optimizing four highly multimodal functions (Michalewicz's F1 and F2, Branin's Rcos, Griewank's). The genotypic and phenotypic approaches were implemented using the Grefenstette's bias b and the stability of mean population fitness as measures of convergence, respectively. Quality of results and speed of convergence are the main perfomance variables contrasted.