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Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population of programs. It applies the genetic operators selection according to a predefined fitness measure, mutation and crossover.
History, Applications & Art
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programming genetic gp programs mutation crossover program tree generation subtree new selection representations evolution fitness may also randomly child individuals
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Genetic programming | is a | proposed meta-learning technique of evolving a genetic programming system using genetic programming itself | 0.90 | text |
| fitness proportionate selection | instance of | although other methods | 0.80 | text |
| lexicase selection | instance of | although other methods | 0.80 | text |
| and others have been demonstrated to perform better for many GP problems.Elitism | instance of | although other methods | 0.80 | text |
| which involves seeding the next generation with the best individual | instance of | although other methods | 0.80 | text |
| Genetic programming | has application | GP | 0.60 | section |
| Genetic programming | has application | Some | 0.60 | section |
| Genetic programming | has application | John | 0.60 | section |
| Genetic programming | has application | Koza | 0.60 | section |
| Genetic programming | has application | Since | 0.60 | section |
| Genetic programming | has application | Genetic | 0.60 | section |
| Genetic programming | has application | Evolutionary Computation Conference | 0.60 | section |
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