Abstract
In this work, we present a generalisation to continuous domains of an optimization method based on evolutionary computation that applies Bayesian classifiers in the learning process. The main difference between other estimation of distribution algorithms (EDAs) and this new method –known as Evolutionary Bayesian Classifier-based Optimization Algorithms (EBCOAs)– is the way the fitness function is taken into account, as a new variable, to generate the probabilistic graphical model that will be applied for sampling the next population.
We also present experimental results to compare performance of this new method with other methods of the evolutionary computation field like evolution strategies, and EDAs. Results obtained show that this new approach can at least obtain similar performance as these other paradigms.
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Miquélez, T., Bengoetxea, E., Larrañaga, P. (2006). Evolutionary Bayesian Classifier-Based Optimization in Continuous Domains. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_67
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DOI: https://doi.org/10.1007/11903697_67
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