Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Algoritm Genetic Deterministic× | Recalire simulată× | |
|---|---|---|
| Domeniu≠ | Simulare | Optimizare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1975–1989 | 1983 |
| Autorul original≠ | Goldberg, D. E.; Holland, J. H. | — |
| Tip≠ | Deterministic evolutionary optimization | Probabilistic metaheuristic / local search |
| Sursa seminală≠ | Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. ISBN: 9780201157673 | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗ |
| Denumiri alternative≠ | DGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GA | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search |
| Înrudite | 5 | 5 |
| Rezumat≠ | A Deterministic Genetic Algorithm (DGA) applies the structural framework of evolutionary computation — population, selection, crossover, and replacement — using entirely deterministic operators and fixed decision rules instead of stochastic sampling. By eliminating randomness, the algorithm becomes fully reproducible: running it twice on the same problem yields identical solutions, making it tractable for rigorous benchmarking, reproducibility studies, and systems where stochasticity is undesirable. | 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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