Abstract
In this paper we compare two strategies using locally weighted regression as a surrogate model to improve the efficiency of a real-coded generational genetic algorithm where a fixed budget of simulations is imposed. Only a fraction of the candidate solutions are evaluated exactly, allowing for more generations to evolve the population (the number of generations increases according to a user defined parameter). We test the proposed strategies on a set of benchmark optimization problems from the literature. The results show that the surrogate strategies can improve the performance of the GA depending on the user defined parameter. We suggest a threshold value to this parameter so that the locally weighted regression can be used to enhance the efficiency of genetic algorithms, when the number of calls to the expensive simulation is limited.
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Fonseca, L.G., Bernardino, H.S., Barbosa, H.J.C. (2012). A Genetic Algorithm Assisted by a Locally Weighted Regression Surrogate Model. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31125-3_10
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DOI: https://doi.org/10.1007/978-3-642-31125-3_10
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