Abstract
The previous chapter showed that variation operators in genetic and evolutionary algorithms can be replaced by learning a probabilistic model of selected solutions and sampling the model to generate new candidate solutions. Algorithms based on this principle are called probabilistic model-building genetic algorithms ⦓PMBGAs) [133]. This chapter reviews most influential PMBGAs and discusses their strengths and weaknesses. The chapter focuses on PMBGAs working in a discrete domain but other representations are also discussed briefly.
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Pelikan, M. Probabilistic Model-Building Genetic Algorithms. In: Hierarchical Bayesian Optimization Algorithm. Studies in Fuzziness and Soft Computing, vol 170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32373-0_2
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DOI: https://doi.org/10.1007/978-3-540-32373-0_2
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23774-7
Online ISBN: 978-3-540-32373-0
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