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Time Consuming Numerical Model Calibration Using Genetic Algorithm (GA), 1-Nearest Neighbor (1NN) Classifier and Principal Component Analysis (PCA)

机译：使用遗传算法（GA），最近邻（1NN）分类器和主成分分析（PCA）进行的耗时数值模型校准

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Single objective genetic algorithm (SGA) optimization process usually needs a large number of objective function evaluations before converging towards global optimum or a near-optimum. The SGA is used as automatic calibration method for a wide range of numerical models. However, the evaluation of the quality of solutions is very time-consuming in many real-world numerical model calibration problems. The algorithm SGA-INN-PCA, an effective and efficient dynamic approximation model to reduce the number of actual fitness evaluations, is presented in this paper. Training data of 1NN classifier are produced from early generations. 1-nearest neighbor (INN) classifier is used to predict objective function values for evaluations. Principal component analysis (PCA) linearly transforms high-dimensional optimization parameters into low-dimensional optimization parameters to save test time for 1NN. The test results show that the proposed method only requires about 25 percent of actual fitness evaluations of the SGA

机译：单个客观遗传算法（SGA）优化过程通常需要大量的客观函数评估，然后融合到全球最佳或近最佳。 SGA用作各种数值模型的自动校准方法。然而，在许多真实的数值模型校准问题中，对解决方案质量的评估非常耗时。本文介绍了算法SGA-INN-PCA，有效高效的动态近似模型，以减少实际健身评估数量。 1NN分类器的训练数据是从早期的几代生产的。 1 - 最近的邻居（INN）分类器用于预测评估的客观函数值。主成分分析（PCA）线性将高维优化参数线性变换为低维优化参数，以节省1NN的测试时间。测试结果表明，该方法仅需要大约25％的SGA实际健身评估

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《》|2005年|1208-1211|共4页
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Yang Liu; Wen-Jing Ye;
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calibration; genetic algorithms; principal component analysis; 1-nearest neighbor classifier; 1NN; PCA; SGA; principal component analysis; single objective genetic algorithm optimization; time consuming numerical model calibration;

机译：校正;遗传算法;主要成分分析; 1-最近邻分类器; 1NN; PCA; SGA;主要成分分析;单目标遗传算法优化;耗时的数值模型校正;

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Time Consuming Numerical Model Calibration Using Genetic Algorithm (GA), 1-Nearest Neighbor (1NN) Classifier and Principal Component Analysis (PCA)

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