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Genetic programming


In artificial intelligence, genetic programming (GP) is a technique whereby computer programs are encoded as a set of genes that are then modified (evolved) using an evolutionary algorithm (often a genetic algorithm, "GA") – it is an application of (for example) genetic algorithms where the space of solutions consists of computer programs. The results are computer programs able to perform well in a predefined task. The methods used to encode a computer program in an artificial chromosome and to evaluate its fitness with respect to the predefined task are central in the GP technique and still the subject of active research.

In 1954, pioneering work on what is today known as artificial life was carried out by Nils Aall Barricelli using the very early computers. 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. The establishment of evolutionary algorithms in the scientific community allowed, by then, the first concrete steps to study the GP idea.

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. 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). 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.Gianna Giavelli, a student of Koza's, later pioneered the use of genetic programming as a technique to model DNA expression.


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