Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Programmation Linéaire en Nombres Entiers× | Algorithme génétique× | |
|---|---|---|
| Domaine≠ | Simulation | Optimisation |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1958–1960 | 1975 |
| Auteur d'origine≠ | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) | John Henry Holland |
| Type≠ | Mathematical optimization | Population-based metaheuristic |
| Source fondatrice≠ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| Alias≠ | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally. | 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. |
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