Abstract
We investigate the in.uence of model bias in model-based search. As an example we choose Ant Colony Optimization as a wellknown model-based search algorithm. We present the effect of two different pheromone models for an Ant Colony Optimization algorithm to tackle a general scheduling problem. The results show that a pheromone model can introduce a strong bias toward certain regions of the search space, stronger than the selection pressure introduced by the updating rule for the model. This potentially leads to an algorithm where over time the probability to produce good quality solutions decreases.
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Blum, C., Sampels, M. (2002). When Model Bias Is Stronger than Selection Pressure. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_86
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DOI: https://doi.org/10.1007/3-540-45712-7_86
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