|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Fitness modeling with markov networks|
|Keywords:||Estimation of distribution algorithms|
Markov random fields
|Citation:||Brownlee A, McCall J & Zhang Q (2013) Fitness modeling with markov networks, IEEE Transactions on Evolutionary Computation, 17 (6), pp. 862-879.|
|Abstract:||Fitness modelling has received growing interest from the evolutionary computation community in recent years. With a fitness model, one can improve evolutionary algorithm efficiency by directly sampling new solutions, developing hybrid guided evolutionary operators or using the model as a surrogate for an expensive fitness function. This paper addresses several issues on fitness modelling of discrete functions, in particular how modelling quality and efficiency can be improved. We define the Markov network fitness model (MFM) in terms of Walsh functions. We explore the relationship between the MFM and fitness in a number of discrete problems, showing how the parameters of the fitness model can identify qualitative features of the fitness function. We define the fitness prediction correlation, a metric to measure fitness modelling capability of local and global fitness models. We use this metric to investigate the effects of population size and selection on the trade-off between model quality and complexity for the MFM.|
|Rights:||The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.|
|mn-fitnessmodelling.pdf||665.97 kB||Adobe PDF||Under Permanent Embargo Request a copy|
Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact firstname.lastname@example.org providing details and we will remove the Work from public display in STORRE and investigate your claim.