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Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Στοχαστικός Γενετικός Αλγόριθμος× | Γενετικός Αλγόριθμος× | |
|---|---|---|
| Πεδίο≠ | Προσομοίωση | Βελτιστοποίηση |
| Οικογένεια | Process / pipeline | Process / pipeline |
| Έτος προέλευσης | 1975 | 1975 |
| Δημιουργός≠ | Holland, J. H. | John Henry Holland |
| Τύπος≠ | Stochastic evolutionary metaheuristic | Population-based metaheuristic |
| Θεμελιώδης πηγή≠ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110 | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| Εναλλακτικές ονομασίες≠ | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Συναφείς | 5 | 5 |
| Σύνοψη≠ | 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. | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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