قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| البرمجة الصحيحة المختلطة× | الخوارزمية الجينية× | |
|---|---|---|
| المجال≠ | المحاكاة | التحسين |
| العائلة | Process / pipeline | Process / pipeline |
| سنة النشأة≠ | 1958–1960 | 1975 |
| صاحب الطريقة≠ | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) | John Henry Holland |
| النوع≠ | Mathematical optimization | Population-based metaheuristic |
| المصدر التأسيسي≠ | 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 ↗ |
| الأسماء البديلة≠ | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| ذات صلة≠ | 6 | 5 |
| الملخص≠ | 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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