Summary
This chapter introduces a new Evolutionary Computation method which applies Bayesian classifiers in the construction of a probabilistic graphical model that will be used to evolve a population of individuals. On the other hand, the satisfiability problem (SAT) is a central problem in the theory of computation as a representative example of NP-complete problems. We have verified the performance of this new method for the SAT problem. We compare three different solution representations suggested in the literature. Finally, we apply local search methods for this problem.
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Miqu�lez, T., Bengoetxea, E., Larra�aga, P. (2006). Bayesian Classifiers in Optimization: An EDA-like Approach. In: Lozano, J.A., Larra�aga, P., Inza, I., Bengoetxea, E. (eds) Towards a New Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32494-1_9
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DOI: https://doi.org/10.1007/3-540-32494-1_9
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