A Dynamic Meta-Model Approach to Genetic Algorithm Solution of a Risk-Based Groundwater Remediation Design Model
A Dynamic Meta-Model Approach to Genetic Algorithm Solution of a Risk-Based Groundwater Remediation Design Model
复制标题
基于风险的地下水修复设计模型遗传算法求解的动态元模型方法
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
B. Minsker
中科院分区：
文献类型：
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作者：
S. Yan;B. Minsker
近似（“元”）模型已用于耦合优化和仿真模型，以提高计算效率。在大多数情况下，在优化之前已经完成了多次模拟运行，这些模拟运行用于拟合随后用于优化的近似模型。在本研究中，我们提出了一种动态元建模方法，其中将人工神经网络（ANN）和支持向量机（SVM）嵌入到遗传算法（GA）优化框架中，以取代耗时的流量和污染物传输模型。对早期 GA 生成的数据进行采样以训练 ANN 和 SVM，并定期调用数值模型以动态更新 ANN 和 SVM。这使得元模型能够适应 GA 搜索的区域并提供更高的准确性。初步结果表明，训练有素的 ANN 或 SVM 可以达到令人满意的精度。会议将介绍不同的动态训练方法。
Approximation ("meta") models have been used in coupled optimization and simulation models to improve computational efficiency. In most instances, multiple simulation runs have been done before the optimization, which are used to fit an approximate model that is then used for the optimization. In this study, we propose a dynamic meta-modeling approach, in which artificial neural networks (ANN) and support vector machines (SVM) are embedded into a genetic algorithm (GA) optimization framework to replace time-consuming flow and contaminant transport models. Data produced from early generations of the GA are sampled to train the ANN and SVM and the numerical models are periodically called to dynamically update the ANN and SVM. This allows the meta model to adapt to the area in which the GA is searching and provide more accuracy. Preliminary results show that a well trained ANN or SVM can achieve satisfactory accuracy. Different approaches to dynamic training will be presented at the conference.