Methoden vergleichen
Prüfen Sie die ausgewählten Methoden nebeneinander; abweichende Zeilen sind hervorgehoben.
| Deterministischer Genetischer Algorithmus× | Genetischer Algorithmus× | |
|---|---|---|
| Fachgebiet≠ | Simulation | Optimierung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1975–1989 | 1975 |
| Urheber≠ | Goldberg, D. E.; Holland, J. H. | John Henry Holland |
| Typ≠ | Deterministic evolutionary optimization | Population-based metaheuristic |
| Wegweisende Quelle≠ | Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. ISBN: 9780201157673 | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| Aliasnamen≠ | DGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GA | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | 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. |
| ScholarGateDatensatz ↗ |
|
|