Genetic Programming of Logic-Based Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.9262

@InCollection{VGaudet1996,

• author = "Vincent Charles Gaudet",
• title = "Genetic Programming of Logic-Based Neural Networks",
• booktitle = "Genetic Algorithms for Pattern Recognition",
• publisher = "CRC Press",
• year = "1996",
• editor = "Sankar K. Pal and Paul P. Wang",
• chapter = "10",
• pages = "213--226",
• address = "Boca Raton, USA",
• keywords = "genetic algorithms, genetic programming, ANN",
• isbn13 = "9780203713402",
• URL = "https://www.taylorfrancis.com/chapters/edit/10.1201/9780203713402-10/genetic-programming-logic-based-neural-networks-vincent-charles-gaudet",
• DOI = "10.1201/9780203713402",
• abstract = "Genetic algorithms and genetic programming are genetics-based optimization methods in which potential solutions evolve via operators such as selection, crossover, and mutation. Logic-based neural networks are a variation of standard neural networks; they fill the gap between distributed, unstructured neural networks and symbolic programming. The algorithm is meant to be part of a two-level development process where, at first, satisfactory logic-based neural networks are obtained using our algorithm; then, gradient-based learning methods are used to refine the networks. The chapter introduces modifications to the genetic programming paradigm (GPP) that make it possible for the GPP to obtain Logic-based neural networks (LNN). The modifications include a new, more general data structure which is very suitable to LNNs; also, new operators that are suitable to LNNs are developed. The LNNs obtained using the GPP method are starting architectures from which more refined, gradient-based learning is performed using LNN-adapted learning methods.",

}

Genetic Programming entries for Vincent Gaudet

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