Using biology to solve a problem in automated machine learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InCollection{koza:1998:WYNNE,

• author = "John R. Koza",
• title = "Using biology to solve a problem in automated machine learning",
• booktitle = "Models of Action: Mechanisms for Adaptive Behavior",
• publisher = "Lawrence Erlbaum Associates",
• year = "1998",
• editor = "Clive D. L. Wynne and John E. R. Staddon",
• chapter = "5",
• pages = "157--199",
• address = "Hillsdale, NJ, USA",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-8058-1597-X",
• URL = "http://www.genetic-programming.com/jkpdf/wynnestaddon1998.pdf",
• abstract = "This chapter describes how the biological theory of gene duplication described in Susumu Ohno's provocative book, Evolution by Means of Gene Duplication, was brought to bear on a vexatious problem from the domain of automated machine learning. The goal of automatic programming is to create, in an automated way, a computer program that enables a computer to solve a problem. Ideally, an automatic programming system should require that the user pre-specify little about the problem environment. Genetic programming is a domain-independent approach to automated machine learning that attempts to evolve a computer program that solves, or approximately solves, problems. Starting with a primordial ooze of randomly generated computer programs composed of the available programmatic ingredients, genetic programming applies the principles of animal husbandry (including Darwinian selection and sexual recombination) to breed new (and often improved) populations of computer programs. One of the undesirable aspects of many techniques of automated machine learning is that the user of the technique may be required to specify the size and shape (i.e., the architecture) of the ultimate solution to his problem before he can begin to apply the technique to his problem. Specification of the size and shape of the solution often corresponds to discovering a way to decompose the problem into useful subspaces (usually of lower dimensionality) or to discovering a congenial representation of the problem that facilitates solution of the problem. Thus, in practice, for many problems of interest, determining the size and shape of the solution may be the problem (or at least a substantial part of the problem). This chapter describes how biology motivated a solution to the problem of architecture discovery for genetic programming. The resulting biologically-motivated approach enables genetic programming to automatically discover the size and shape of the solution at the same time as genetic programming is evolving a solution to the problem. This is accomplished using six new architecture-altering operations that provide a way to automatically discover, during a run of genetic programming, both the architecture and the sequence of steps of a multi-part computer program that will solve the given problem.",
• notes = "http://www.bookdepository.co.uk/Models-Action-Clive-DL-Wynne/9780805815979?b=-3&t=-26#Bibliographicdata-26",
• size = "75 pages",

}

Genetic Programming entries for John Koza

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