Probabilistic Model-Building Genetic Algorithms
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) . 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.
KeywordsProbabilistic Model Bayesian Network Greedy Algorithm Candidate Solution Probability Vector
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