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The Markov network fitness model

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posted on 2011-10-07, 15:17 authored by Alexander E.I. Brownlee, Siddhartha Shakya, John McCall
Fitness modelling is an area of research which has recently received much interest among the evolutionary computing community. Fitness models can improve the efficiency of optimisation through direct sampling to generate new solutions, guiding of traditional genetic operators or as surrogates for a noisy or long-running fitness functions. In this chapter we discuss the application of Markov networks to fitness modelling of black-box functions within evolutionary computation, accompanied by discussion on the relationship betweenMarkov networks andWalsh analysis of fitness functions.We review alternative fitness modelling and approximation techniques and draw comparisons with the Markov network approach. We discuss the applicability of Markov networks as fitness surrogates which may be used for constructing guided operators or more general hybrid algorithms.We conclude with some observations and issues which arise from work conducted in this area so far.

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Citation

BROWNLEE, A.E.I., SHAKYA, S. and MCCALL, J., 2012. The Markov network fitness model. IN: Shakya, S. and Santana, R. (eds). Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, Vol. 14. London: Springer.

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  • AM (Accepted Manuscript)

Publication date

2012

Notes

This book chapter was accepted for publication in Markov Networks in Evolutionary Computation [© Springer]: http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-28899-9

ISBN

9783642288999

ISSN

1867-4534

Book series

Adaptation, Learning, and Optimization; 14

Language

  • en

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