Process / pipelineSimulation / optimization
确定性遗传算法 — 无随机性的进化优化
确定性遗传算法（DGA）应用了进化计算的结构框架——种群、选择、交叉和替换——但使用完全确定的算子和固定的决策规则，而非随机抽样。通过消除随机性，该算法变得完全可复现：在相同问题上运行两次会产生相同的解，使其适用于严格的基准测试、可复现性研究以及不允许随机性的系统。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. ISBN: 9780201157673
- Mahfoud, S. W. (1995). Niching methods for genetic algorithms. IlliGAL Report No. 95001, University of Illinois at Urbana-Champaign. link ↗
如何引用本页
ScholarGate. (2026, June 3). Deterministic Genetic Algorithm — Evolutionary optimization with deterministic selection and operators. ScholarGate. https://scholargate.app/zh/simulation/deterministic-genetic-algorithm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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