A Survey of Optimization by Building and Using Probabilistic Models

Authors:
Martin Pelikan;David E. Goldberg;Fernando G. Lobo
Affiliations:
Illinois Genetic Algorithms Laboratory, Department of General Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. pelikan@illigal.ge.uiuc.edu;Illinois Genetic Algorithms Laboratory, Department of General Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. deg@illigal.ge.uiuc.edu;Illinois Genetic Algorithms Laboratory, Department of General Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. lobo@illigal.ge.uiuc.edu
Venue:
Computational Optimization and Applications
Year:
2002

Citing 15
Cited 177

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Abstract

This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space. It settles the algorithms in the field of genetic and evolutionary computation where they have been originated, and classifies them into a few classes according to the complexity of models they use. Algorithms within each class are briefly described and their strengths and weaknesses are discussed.