השוואת שיטות

	אלגוריתם גנטי סטוכסטי	אופטימיזציה סטוכסטית מרובת יעדים
תחום	סימולציה	סימולציה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1975	1990s–2000s
הוגה השיטה	Holland, J. H.	Various (Fonseca, Fleming, Deb, Zitzler, and others)
סוג	Stochastic evolutionary metaheuristic	Stochastic metaheuristic optimization
מקור מכונן	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
כינויים	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization
קשורות	5	5
תקציר	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.