Genetic programming

Topic | v1 | created by jjones |

In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs. It is essentially a heuristic search technique often described as 'hill climbing', i.e. searching for an optimal or at least suitable program among the space of all programs. The operations are: selection of the fittest programs for reproduction (crossover) and mutation according to a predefined fitness measure, usually proficiency at the desired task. The crossover operation involves swapping random parts of selected pairs (parents) to produce new and different offspring that become part of the new generation of programs. Mutation involves substitution of some random part of a program with some other random part of a program.


subtopic of Artificial intelligence (AI)

Artificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligen...

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treated in Genetic programming - on the programming of computers by means of natural selection

Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduc...