قارن الطرق

	الخوارزمية الجينية العشوائية	التحسين العشوائي متعدد الأهداف
المجال	المحاكاة	المحاكاة
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1975	1990s–2000s
صاحب الطريقة	Holland, J. H.	Various (Fonseca, Fleming, Deb, Zitzler, and others)
النوع	Stochastic evolutionary metaheuristic	Stochastic metaheuristic optimization
المصدر التأسيسي	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
الأسماء البديلة	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization
ذات صلة	5	5
الملخص	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.