Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Ģenētiskais algoritms× | Hiperheuristikas× | |
|---|---|---|
| Nozare | Optimizācija | Optimizācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1975 | 2013 |
| Autors≠ | John Henry Holland | Burke et al. |
| Tips≠ | Population-based metaheuristic | High-level search methodology |
| Pirmavots≠ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ | Burke, E. K., et al. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695–1724. DOI ↗ |
| Citi nosaukumi≠ | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon | Heuristic of Heuristics, Algorithm Selection Hyper-Heuristic, Selection Hyper-Heuristic, Hiyer-Sezgisel |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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. | Hyper-heuristics are high-level methodologies that search over a space of heuristics rather than directly over the space of solutions. Introduced systematically by Burke et al. (2013) in their landmark survey, hyper-heuristics operate by selecting or generating low-level heuristics to solve hard combinatorial optimisation and search problems, aiming to automate the design of optimisation algorithms across diverse problem domains without requiring deep problem-specific knowledge. |
| ScholarGateDatu kopa ↗ |
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