Parameter Control in Evolutionary Algorithms

Aguston Eiben (1) , Zbigniew Michalewicz (2) , Marc Schoenauer (3) , Jim Smith (4)

1 VU - Computational Intelligence Group
2 School of Computer Science [Adelaide]
3 TANC - Algorithmic number theory for cryptology
4 Faculty of Computing, Engineering and Mathematical Sciences

The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classification of different approaches based on a number of complementary features, and pay special attention to setting parameters on-the-fly. This has the potential of adjusting the algorithm to the problem while solving the problem. This paper is intended to present a survey rather than a set of prescriptive details for implementing an EA for a particular type of problem. For this reason we have chosen to interleave a number of examples throughout the text. Thus we hope to both clarify the points we wish to raise as we present them, and also to give the reader a feel for some of the many possibilities available for controlling different parameters.

Domaines: Intelligence artificielle [cs.AI]

Aguston Eiben, Zbigniew Michalewicz, Marc Schoenauer, Jim Smith. Parameter Control in Evolutionary Algorithms. Fernando G. Lobo and Cláudio F. Lima and Zbigniew Michalewicz. Parameter Setting in Evolutionary Algorithms, 54 (54), Springer Verlag, pp.19-46, 2007, Studies in Computational Intelligence, 978-3-540-69431-1. ⟨10.1007/978-3-540-69432-8⟩. ⟨inria-00140549⟩