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.
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Kötzing, T., Molter, H. (2012). ACO Beats EA on a Dynamic Pseudo-Boolean Function. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32937-1_12
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DOI: https://doi.org/10.1007/978-3-642-32937-1_12
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