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. © 2012 Springer-Verlag.
CITATION STYLE
Kötzing, T., & Molter, H. (2012). ACO beats EA on a dynamic pseudo-boolean function. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7491 LNCS, pp. 113–122). https://doi.org/10.1007/978-3-642-32937-1_12
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