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Genetic programming uses methods which are similar to [[genetic algorithm]]s (GA), but is based on programs which perform tasks whose results can then be evaluated via a fitness function, as in a GA. Instead of using populations of parameter lists to be evaluated by some evaluation procedure, GP uses populations of programs which are to be run to perform the required task. A technical difference between GAs and GPs is that GAs use list structures, often of fixed size, to store their data, while GPs use tree structures which can vary in size and shape.
The application of a tree representation (and required genetic operators) for using genetic algorithms to generate programs was first described in 1985 by Cramer. Koza, though he did not first explore genetic programming, is indisputably the field's most prolific and persuasive author. Koza and other early GP researchers used the artificial inteligence language [[Lisp_programming_language|Lisp]] to program their GPs.
GPs is very computationally intensive and so in the 1990s it was mainly used to solve relatively simple problems. However, more recently, thanks to various improvements in GP technology and to the well known exponential growth in CPU power, GP has started delivering a number of outstanding results. At the time of writing over 40 human-competitive results have been gathered, in areas such as quantum computing, electronic design, game playing, sorting, searching and many more. These results include the replication or infringement of several post-year-2000 inventions, and the production of two patentable new inventions.
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