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Stochastic Genetic Algorithm

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.

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Stochastic Genetic Algorithm — Randomized evolutionary search for combinatorial and continuous optimization

Taxonomisk metodpost · process-pipeline / simulation

Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. · ISBN 978-0262581110
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. · ISBN 978-0201157673

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