Self-tuning mechanism for genetic algorithms parameters, an application to data-object allocation in the web

Abstract

In this paper, a new mechanism for automatically obtaining some control parameter values for Genetic Algorithms is presented, which is independent of problem domain and size. This approach differs from the traditional methods which require knowing first the problem domain, and then knowing how to select the parameter values for solving specific problem instances. The proposed method is based on a sample of problem instances, whose solution permits to characterize the problem and to obtain the parameter values.To test the method, a combinatorial optimization model for data-objects allocation in the Web (known as DFAR) was solved using Genetic Algorithms. We show how the proposed mechanism permits to develop a set of mathematical expressions that relates the problem instance size to the control parameters of the algorithm. The experimental results show that the self-tuning of control parameter values of the Genetic Algorithm for a given instance is possible, and that this mechanism yields satisfactory results in quality and execution time. We consider that the proposed method principles can be extended for the self-tuning of control parameters for other heuristic algorithms. © Springer-Verlag Berlin Heidelberg 2004.