Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Stochastic Genetic Algorithm× | Simulated Annealing× | |
|---|---|---|
| Nozare≠ | Simulācija | Optimizācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1975 | 1983 |
| Autors≠ | Holland, J. H. | — |
| Tips≠ | Stochastic evolutionary metaheuristic | Probabilistic metaheuristic / local search |
| Pirmavots≠ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110 | Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI ↗ |
| Citi nosaukumi≠ | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Stochastic Genetic Algorithm (SGA) is a population-based metaheuristic that mimics biological evolution — selection, crossover, and mutation — to search for near-optimal solutions in complex, nonlinear, or combinatorial spaces. Its randomized operators make it robust to local optima and broadly applicable across engineering, scheduling, machine learning, and operations research. | 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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