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In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task. Essentially GP is a set of instructions and a fitness function to measure how well a computer has performed a 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. ==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 finite-state automata. Later GP-related work grew out of the learning classifier system community, which developed sets of sparse rules describing optimal policies for Markov decision processes. In 1981 (Richard Forsyth ) evolved tree rules to classify heart disease.〔(Kybernetes 1981 )〕 The first statement of modern "tree-based" genetic programming (that is, procedural languages organized in tree-based structures and operated on by suitably defined GA-operators) was given by Nichael L. Cramer (1985).〔(Cramer, 1985 )〕 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.〔(genetic-programming.com-Home-Page )〕 Gianna Giavelli, a student of Koza's, later pioneered the use of genetic programming as a technique to model DNA expression.〔The Genetic Coding of Behavioral Attributes in Cellular Automata. Artificial Life at Stanford 1994 Stanford, California, 94305-3079 USA.〕 In the 1990s, GP was mainly used to solve relatively simple problems because it is very computationally intensive. Recently GP has produced many novel and outstanding results in areas such as quantum computing, electronic design, game playing, sorting, and searching, due to improvements in GP technology and the exponential growth in CPU power.〔 John R. Koza. ("36 Human-Competitive Results Produced by Genetic Programming" ). retrieved 2015-09-01. 〕 These results include the replication or development of several post-year-2000 inventions. GP has also been applied to evolvable hardware as well as computer programs. Developing a theory for GP has been very difficult and so in the 1990s GP was considered a sort of outcast among search techniques. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Genetic programming」の詳細全文を読む スポンサード リンク
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