विधियों की तुलना करें

	अनिश्चितता के तहत बहु-उद्देश्यीय इष्टतमीकरण	स्टोकेस्टिक जेनेटिक एल्गोरिथम
क्षेत्र	अनुकरण	अनुकरण
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1990s–2000s	1975
प्रवर्तक	Various (Fonseca, Fleming, Deb, Zitzler, and others)	Holland, J. H.
प्रकार	Stochastic metaheuristic optimization	Stochastic evolutionary metaheuristic
मौलिक स्रोत	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110
उपनाम	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm
संबंधित	5	5
सारांश	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.	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.