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Probabilistic Model-Building Genetic Algorithms

  • Martin Pelikan
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 170)

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.

Keywords

Probabilistic Model Bayesian Network Greedy Algorithm Candidate Solution Probability Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  • Martin Pelikan
    • 1
  1. 1.Dept. of Mathematics and Computer ScienceUniversity of MissouriSt. Louis, MOUSA

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