« Genetic operator » : différence entre les versions
m (Remplacement de texte — « <small>Entrez ici les domaines et catégories...</small> » par « ») |
m (Remplacement de texte — « <br/> » par « ») |
||
| Ligne 20 : | Ligne 20 : | ||
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.[1] | In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.[1] | ||
Version du 7 mai 2020 à 13:23
en construction
Définition
Français
Anglais
Genetic algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.[1]
Contributeurs: wiki
