Process / pipelineSimulation / optimization
贝叶斯遗传算法 — 基于概率模型的进化优化
贝叶斯遗传算法（BGA）用从高适应度个体中学习到的概率贝叶斯网络取代了传统的交叉和变异算子。在每一代，算法都会构建一个有希望的解结构的图模型，然后从该模型中采样新的后代，从而使搜索能够捕获和利用标准遗传算法所遗漏的变量依赖关系。
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来源
- Pelikan, M., Goldberg, D. E., & Cantu-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), pp. 525–532. Morgan Kaufmann. link ↗
- Larranaga, P., & Lozano, J. A. (Eds.) (2002). Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Boston. ISBN: 9781461352747
如何引用本页
ScholarGate. (2026, June 3). Bayesian Genetic Algorithm — Probabilistic model-guided evolutionary optimization. ScholarGate. https://scholargate.app/zh/simulation/bayesian-genetic-algorithm
Which method?
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