مقایسهٔ روش‌ها

	الگوریتم ژنتیک	برنامه‌ریزی هدف	برنامه‌ریزی عدد صحیح مختلط
حوزه	بهینه‌سازی	تصمیم‌گیری	شبیه‌سازی
خانواده	Process / pipeline	MCDM	Process / pipeline
سال پیدایش	1975	1955	1958–1960
پدیدآور	John Henry Holland	Charnes, A., Cooper, W. W.	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
نوع	Population-based metaheuristic	Multi-objective optimisation — weighted/lexicographic goal deviation minimisation	Mathematical optimization
منبع بنیادین	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
نام‌های دیگر	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	—	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
مرتبط	5	8	6
خلاصه	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.	GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	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.