Genetic micro programming of Neural Networks

title = "Genetic micro programming of Neural Networks",

author = "Frederic Gruau",

booktitle = "Advances in Genetic Programming",

publisher = "MIT Press",

editor = "Kenneth E. {Kinnear, Jr.}",

year = "1994",

pages = "495--518",

chapter = "24",

keywords = "genetic algorithms, genetic programming, ANN",

URL = "

http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888",

URL = "

http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap24.pdf",

DOI = "

doi:10.7551/mitpress/1108.003.0030",

size = "24 pages",

abstract = "Cellular Encoding is a method for encoding families of similarly structured Boolean neural networks, that can compute scalable boolean functions. Genetic Programming uses the Genetic Algorithm to evolve LISP computer programs. This chapter demonstrates that Cellular Encoding is a micro-programming language of neural networks and that genetic search of neural networks using Cellular Coding is equivalent to Genetic Micro Programming. The concept of genetic language is defined. Cellular Encoding and LISP are two particular Genetic Programming languages. Other programming languages are proposed. A criterion is put forward to classify genetic languages with increasing complexity. With respect to this criterion Lisp is more complex than Cellular Encoding. Which language is better for Genetic Programming? We argue that Cellular Encoding is better than LISP for the synthesis is of neural networks, and LISP is better for symbolic manipulation. Ultimately, it is possible to evolve the genetic language itself.",

notes = "Part of \cite{kinnear:book}",