ACO Beats EA on a Dynamic Pseudo-Boolean Function

Timo Kötzing¹ and Hendrik Molter²

¹Max Planck Institute for Informatics, Saarbrücken, Germany

²Saarland University, Saarbrücken, Germany

Abstract. In this paper, we contribute to the understanding of the behavior of bio-inspired algorithms when tracking the optimum of a dynamically changing fitness function over time. In particular, we are interested in the difference between a simple evolutionary algorithm (EA) and a simple ant colony optimization (ACO) system on deterministically changing fitness functions, which we call dynamic fitness patterns. Of course, the algorithms have no prior knowledge about the patterns.

We construct a bit string optimization problem where we can show that the ACO system is able to follow the optimum while the EA gets lost.

LNCS 7491, p. 113 ff.

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