Linganisha mbinu

	Uboreshaji wa Malengo Mengi ya Kistochastiki	Algorithmu ya Kijenetiki ya Kistokastiki
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1990s–2000s	1975
Mwanzilishi	Various (Fonseca, Fleming, Deb, Zitzler, and others)	Holland, J. H.
Aina	Stochastic metaheuristic optimization	Stochastic evolutionary metaheuristic
Chanzo asilia	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
Majina mbadala	SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm
Zinazohusiana	5	5
Muhtasari	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.