By Topic

Design architectures and training of neural networks with a distributed genetic algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Oliker, S. ; Tel Aviv Univ., Ramat Aviv, Israel ; Furst, M. ; Maimon, C.

Designing and training neural networks using a distributed genetic algorithm reinforced by the perceptron learning rule is shown. The method sets the neural network's architecture and weights for a given task where the network is comprised of binary linear threshold units. The search space is not of all the possible nets, but is specified for the unit. Each individual unit is inspected under the restriction of a feedforward network structure in order to find its optimal set of connections and associated weights, related to the present net state. For the genetic algorithm, an objective function (fitness) is defined. It considers for each unit primarily the overall network error, and, secondarily, the unit's possible connections and weights that are preferable for continuity of the convergence process. The perceptron learning rule is used to search a better unit input connection weights set. Examples are given showing the potential of the proposed approach

Published in:

Neural Networks, 1993., IEEE International Conference on

Date of Conference: