Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Algorithme Génétique Stochastique× | Optimisation stochastique multi-objectifs× | |
|---|---|---|
| Domaine | Simulation | Simulation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1975 | 1990s–2000s |
| Auteur d'origine≠ | Holland, J. H. | Various (Fonseca, Fleming, Deb, Zitzler, and others) |
| Type≠ | Stochastic evolutionary metaheuristic | Stochastic metaheuristic optimization |
| Source fondatrice≠ | Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Alias | SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty. |
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