Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/22466
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Swingler, Kevin
Smith, Leslie
Contact Email: kms@cs.stir.ac.uk
Title: An analysis of the local optima storage capacity of Hopfield network based fitness function models
Editors: Nguyen, NT
Kowalczyk, R
Fred, A
Joaquim, F
Citation: Swingler K & Smith L (2014) An analysis of the local optima storage capacity of Hopfield network based fitness function models, Nguyen NT, Kowalczyk R, Fred A, Joaquim F (ed.) Transactions on Computational Collective Intelligence XVII, Berlin Heidelberg: Springer, pp. 248-271.
Issue Date: 2014
Series/Report no.: Lecture Notes in Computer Science, 8790
Abstract: A Hopfield Neural Network (HNN) with a new weight update rule can be treated as a second order Estimation of Distribution Algorithm (EDA) or Fitness Function Model (FFM) for solving optimisation problems. The HNN models promising solutions and has a capacity for storing a certain number of local optima as low energy attractors. Solutions are generated by sampling the patterns stored in the attractors. The number of attractors a network can store (its capacity) has an impact on solution diversity and, consequently solution quality. This paper introduces two new HNN learning rules and presents the Hopfield EDA (HEDA), which learns weight values from samples of the fitness function. It investigates the attractor storage capacity of the HEDA and shows it to be equal to that known in the literature for a standard HNN. The relationship between HEDA capacity and linkage order is also investigated.
Type: Conference Paper
Status: Book Chapter: author post-print (pre-copy editing)
Rights: Published in Nguyen NT, Kowalczyk R, Fred A, Joaquim F (ed.) Transactions on Computational Collective Intelligence XVII by Springer. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-662-44994-3_13
URI: http://hdl.handle.net/1893/22466
URL: http://link.springer.com/chapter/10.1007/978-3-662-44994-3_13
Affiliation: Computing Science - CSM Dept
Computing Science - CSM Dept

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