Genetic Operators with Dynamic Biases that Operate on Attribute Grammar Representations of Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{hussain:1999:G,

• author = "Talib S. Hussain and Roger A. Browse",
• title = "Genetic Operators with Dynamic Biases that Operate on Attribute Grammar Representations of Neural Networks",
• booktitle = "Advanced Grammar Techniques Within Genetic Programming and Evolutionary Computation",
• year = "1999",
• editor = "Talib S. Hussain",
• pages = "83--86",
• address = "Orlando, Florida, USA",
• month = "13 " # jul,
• keywords = "genetic algorithms, genetic programming, grammar, ANN",
• URL = "https://drive.google.com/open?id=1Bv4in0ph5WxUvX8FXBATioVD7MsWwX2u",
• size = "4 pages",
• abstract = "Grammar-based representations of neural networks have shown promise in advancing the study of the evolutionary optimization of neural networks (Yao, 1993; Gruau, 1995; Hussain and Browse, 1998). Our research on the Network Generating Attribute Grammar Encoding (NGAGE) technique has demonstrated that attribute grammars may be used successfully in representing and exploring a space of neural networks (Browse, Hussain and Smillie, 1999). In addition to offering the capability of representing a wide variety of neural network models, NGAGE also offers the potential of designing meaningful dynamic genetic operators. In this paper, we present two reproduction operators that perform a biased offspring creation, and use knowledge of the grammar representation to adapt those biases in response to fitness measurements.",
• notes = "GECCO-99WKS Part of wu:1999:GECCOWKS",

}

Genetic Programming entries for Talib S Hussain Roger A Browse

Citations