Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Algoritm Genetic× | Matheuristici: Hibridizarea programării matematice și a metaeristicilor× | |
|---|---|---|
| Domeniu | Optimizare | Optimizare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1975 | 2009 |
| Autorul original≠ | John Henry Holland | Maniezzo, Stützle & Voß |
| Tip≠ | Population-based metaheuristic | Hybrid optimization framework |
| Sursa seminală≠ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ | Maniezzo, V., Stützle, T., & Voß, S. (Eds.). (2009). Matheuristics: Hybridizing Metaheuristics and Mathematical Programming. Springer. ISBN: 978-1-4419-1305-0 |
| Denumiri alternative≠ | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon | Hybrid Metaheuristics, MIP-based Heuristics, Math-Programming Hybrids, Matematiksel Sezgisel Yöntemler |
| Înrudite≠ | 5 | 3 |
| Rezumat≠ | 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. | Matheuristics is a class of hybrid optimization methods that tightly couple exact mathematical programming components—such as mixed-integer programming (MIP) solvers—with metaheuristic search procedures. Formally introduced and named by Maniezzo, Stützle, and Voß in 2009, the framework leverages the global-search capability of metaheuristics and the structural exploitation of mathematical programming to tackle large-scale combinatorial optimization problems that neither approach can solve effectively alone. |
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