The genetic programming paradigm: Genetically breeding populations of computer programs to solve problems

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

@InCollection{Koza:1992:GPdgc,

• author = "John R. Koza",
• title = "The genetic programming paradigm: Genetically breeding populations of computer programs to solve problems",
• booktitle = "Dynamic, Genetic, and Chaotic Programming",
• publisher = "John Wiley",
• year = "1992",
• editor = "Branko Soucek and the IRIS Group",
• pages = "203--321",
• address = "New York",
• keywords = "genetic algorithms, genetic programming",
• ISBN-13 = "978-0-471-55717-3",
• URL = "http://www.genetic-programming.com/jkpdf/soucek1992.pdf",
• URL = "https://www.amazon.co.uk/Dynamic-Genetic-Chaotic-Programming-Technologies/dp/047155717X",
• size = "161 pages",
• abstract = "Many seemingly different problems in machine learning, artificial intelligence, and symbolic processing can be viewed as requiring the discovery of a computer program that produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible computer programs for a highly fit individual computer program. The recently developed genetic programming paradigm described herein provides a way to search the space of possible computer programs for a highly fit individual computer program to solve (or approximately solve) a surprising variety of different problems from different fields. In the genetic programming paradigm, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (sexual recombination) operator appropriate for genetically mating computer programs. This chapter shows how to reformulate seemingly different problems into a common form (i.e. a problem requiring discovery of a computer program) and, then, to show how the genetic programming paradigm can serve as a single, unified approach for solving problems formulated in this common way.",
• notes = "How can computers learn to program themselves to solve problems?",

}

Genetic Programming entries for John Koza

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