विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| स्टोकेस्टिक जेनेटिक एल्गोरिथम× | सिम्युलेटेड एनीलिंग× | |
|---|---|---|
| क्षेत्र≠ | अनुकरण | अनुकूलन |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1975 | 1983 |
| प्रवर्तक≠ | Holland, J. H. | — |
| प्रकार≠ | Stochastic evolutionary metaheuristic | Probabilistic metaheuristic / local search |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम≠ | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm | Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search |
| संबंधित | 5 | 5 |
| सारांश≠ | 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. |
| ScholarGateडेटासेट ↗ |
|
|