विधियों की तुलना करें

	स्टोकेस्टिक जेनेटिक एल्गोरिथम	अनिश्चितता के तहत बहु-उद्देश्यीय इष्टतमीकरण
क्षेत्र	अनुकरण	अनुकरण
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1975	1990s–2000s
प्रवर्तक	Holland, J. H.	Various (Fonseca, Fleming, Deb, Zitzler, and others)
प्रकार	Stochastic evolutionary metaheuristic	Stochastic metaheuristic optimization
मौलिक स्रोत	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
उपनाम	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization
संबंधित	5	5
सारांश	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.