✦ LIBER ✦

Learning in multilayer perceptrons using global optimization strategies

✍ Scribed by V.P. Plagianakos; G.D. Magoulas; M.N. Vrahatis

Publisher: Elsevier Science
Year: 2001
Tongue: English
Weight: 337 KB
Volume: 47
Category: Article
ISSN: 0362-546X
DOI: 10.1016/s0362-546x(01)00459-x

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✦ Synopsis

Learning algorithms for multilayer perceptrons are usually based on local minimization methods that can be often trapped in a local minimum of the error function. In this work, the use of global optimization strategies for training multilayer perceptrons is investigated. These methods are expected to lead to "optimal" or "near-optimal" weight configurations by allowing the network to escape local minima during training. The paper reviews the fundamentals of a recently proposed deflection procedure, simulated annealing, genetic and evolutionary algorithms, and introduces a new differential evolution strategy. Simulations and comparisons are presented.

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