Genetic Programming

John R. Koza and Riccardo Poli
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John R. Koza: Stanford University
Riccardo Poli: University of Essex, Department of Computer Science

Chapter Chapter 5 in Search Methodologies, 2005, pp 127-164 from Springer

Abstract: Abstract The goal of getting computers to automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called “machine intelligence„ (Turing, 1948, 1950). In his talk entitled AI: Where It Has Been and Where It Is Going, machine learning pioneer Arthur Samuel stated the main goal of the fields of machine learning and artificial intelligence: [T]he aim [is]... to get machines to exhibit behavior, which if done by humans, would be assumed to involve the use of intelligence. (Samuel, 1983) Genetic programming is a systematic method for getting computers to automatically solve a problem starting from a high-level statement of what needs to be done. Genetic programming is a domain-independent method that genetically breeds a population of computer programs to solve a problem. Specifically, genetic programming iteratively transforms a population of computer programs into a new generation of programs by applying analogs of naturally occurring genetic operations. This process is illustrated in Figure 5.1.

Keywords: Genetic Programming; Crossover Point; Fitness Measure; Genetic Operation; Preparatory Step (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1007/0-387-28356-0_5

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