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In artificial intelligence, genetic programming (GP) is an evolutionary algorithmbased methodology inspired by biological evolution to find computer programs that perform a userdefined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. It is a machine learning technique used to optimize a population of computer programs according to a fitness landscape determined by a program's ability to perform a given computational task.
Contents
History
In 1954, GP began with the evolutionary algorithms first used by Nils Aall Barricelli applied to evolutionary simulations. In the 1960s and early 1970s, evolutionary algorithms became widely recognized as optimization methods. Ingo Rechenberg and his group were able to solve complex engineering problems through evolution strategies as documented in his 1971 PhD thesis and the resulting 1973 book. John Holland was highly influential during the 1970s.
In 1964, Lawrence J. Fogel, one of the earliest practitioners of the GP methodology, applied evolutionary algorithms to the problem of discovering finitestate automata. Later GPrelated work grew out of the learning classifier system community, which developed sets of sparse rules describing optimal policies for Markov decision processes. The first statement of modern "treebased" Genetic Programming (that is, procedural languages organized in treebased structures and operated on by suitably defined GAoperators) was given by Nichael L. Cramer (1985),.^{[1]} This work was later greatly expanded by John R. Koza, a main proponent of GP who has pioneered the application of genetic programming in various complex optimization and search problems.^{[2]}
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