Conference Paper (in Formal Publication) ()
Swingler K & Smith L (2014) An analysis of the local optima storage capacity of Hopfield network based fitness function models In: Nguyen NT, Kowalczyk R, Fred A, Joaquim F (ed.) Transactions on Computational Collective Intelligence XVII, Berlin Heidelberg: Springer, pp. 248-271.
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.
Dynamical systems; Hopfield neural networks; Optimization Energy attractors; Fitness function modeling; Fitness functions; Hopfield Networks; Hopfield neural networks (HNN); Optimisation problems; Solution quality; Storage capacity
|Editor||Nguyen NT, Kowalczyk R, Fred A, Joaquim F|
|Authors||Swingler Kevin, Smith Leslie|
|Title of series||Lecture Notes in Computer Science|
|Number in series||8790|
|Date of public distribution||09/2014|
|Place of publication||Berlin Heidelberg|
|ISSN of series||0302-9743|
Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2014)