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Title: The Markov network fitness model
Authors: Brownlee, Alexander E.I.
Shakya, Siddhartha
McCall, John
Issue Date: 2012
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.
Series/Report no.: Adaptation, Learning, and Optimization; 14
Abstract: 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.
Description: 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
Version: Accepted for publication
DOI: 10.1007/978-3-642-28900-2_8
URI: https://dspace.lboro.ac.uk/2134/8916
Publisher Link: http://link.springer.com/chapter/10.1007/978-3-642-28900-2_8
ISBN: 9783642288999
ISSN: 1867-4534
Appears in Collections:Book Chapters (Architecture, Building and Civil Engineering)

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