Brownlee A, McCall J & Shakya SK (2012) The Markov network fitness model. In: Shakya S & Santana R (eds.) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, 14. Berlin Heidelberg: Springer, pp. 125-140. http://link.springer.com/chapter/10.1007/978-3-642-28900-2_8#; https://doi.org/10.1007/978-3-642-28900-2_8
Fitness modelling is an area of research which has recently receivedmuch 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 between Markov 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.
|Title of series||Adaptation, Learning, and Optimization|
|Number in series||14|
|Place of publication||Berlin Heidelberg|
|ISSN of series||1867-4534|