Abstract
The traditional approach for comparing heuristic algorithms uses well-known statistical tests for meaningfully relating the empirical performance of the algorithms and concludes that one outperforms the other. In contrast, the method presented in this paper, builds a predictive model of the algorithms behavior using functions that relate performance to problem size, in order to define dominance regions. This method generates first a representative sample of the algorithms performance, then using a common and simplified regression analysis determines performance functions, which are finally incorporated into an algorithm selection mechanism. For testing purposes, a set of same-class instances of the database distribution problem was solved using an exact algorithm (Branch&Bound) and a heuristic algorithm (Simulated Annealing). Experimental results show that problem size affects differently both algorithms, in such a way that there exist regions where one algorithm is more efficient than the other.
This research was supported in part by CONACYT and COSNET.
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Pérez O., J., Pazos R., R.A., Frausto S., J., Rodríguez O., G., Cruz R., L., Fraire H., H. (2004). Comparison and Selection of Exact and Heuristic Algorithms. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24767-8_43
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DOI: https://doi.org/10.1007/978-3-540-24767-8_43
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