Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Algorithme génétique× | Recuit simulé× | |
|---|---|---|
| Domaine | Optimisation | Optimisation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1975 | 1983 |
| Auteur d'origine≠ | John Henry Holland | — |
| Type≠ | Population-based metaheuristic | Probabilistic metaheuristic / local search |
| Source fondatrice≠ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗ |
| Alias | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search |
| Apparentées | 5 | 5 |
| Résumé≠ | A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail. | Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems. |
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