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The Markov network fitness model
chapter
posted on 2011-10-07, 15:17 authored by Alexander E.I. Brownlee, Siddhartha Shakya, John McCallFitness 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.
History
School
- Architecture, Building and Civil Engineering
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.Version
- AM (Accepted Manuscript)
Publication date
2012Notes
This book chapter was accepted for publication in Markov Networks in Evolutionary Computation [© Springer]: http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-28899-9ISBN
9783642288999ISSN
1867-4534Publisher version
Book series
Adaptation, Learning, and Optimization; 14Language
- en