方法证据记录
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Stochastic Genetic Algorithm — Randomized evolutionary search for combinatorial and continuous optimization
分类方法记录 · 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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