Enzyme Genetic Programming

author = "Michael A. Lones and Andy M. Tyrrell",

title = "Enzyme Genetic Programming",

booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation, CEC 2001",

year = "2001",

pages = "1183--1190",

month = "27--30 " # may,

address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea",

publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",

organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",

publisher = "IEEE Press",

keywords = "genetic algorithms, genetic programming, biomimetic representations, Metabolic Pathways, Evolutionary Electronics",

ISBN = "0-7803-6657-3",

URL = "

http://folk.ntnu.no/lones/lones-cec2001.pdf",

DOI = "

doi:10.1109/CEC.2001.934325",

abstract = "The work reported in this paper follows from the hypothesis that better performance in artificial evolution can be achieved by adhering more closely to the features that make natural evolution effective within biological systems. An important issue in evolutionary computation is the choice of solution representation. Genetic programming, whilst borrowing from biology in the evolutionary axis of behaviour, remains firmly rooted in the artificial domain with its use of a parse tree representation. Following concerns that this approach does not encourage solution evolvability, this paper presents an alternative method modelled upon representations used by biology. Early results are encouraging; demonstrating that the method is competitive when applied to problems in the area of combinatorial circuit design. Whilst too early to gauge its suitability to a more general domain of programming, these results do indicate that the concept of bringing ideas from biological representations to genetic programming is a promising one.",

size = "8 pages",

notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE.

IEEE Catalog Number = 01TH8546C,

Library of Congress Number =",