Prediction based on Kriging with EA-evolving Hyperparameters

版本 1.0.1 (3.5 KB) 作者: Chixin Xiao
This script demonstrates the use of Kriging for prediction, with hyperparameters optimized using an Evolutionary Algorithm (EA).
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更新时间 2024/11/17

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% This script code aims to help my graduates to make sense of the prediction surrogate based on Kriging model( or Guassian model), which
% demonstrates the use of Kriging for prediction, with hyperparameters optimized using an Evolutionary Algorithm (EA).
% The first edition is finished by Chixin Xiao, on 17 Nov 2024, Changsha, China
% Email: chixinxiao@gmail.com
% Synthetic Data Generation: X_samples = -5 + 10 * rand(num_samples, 2).
% Data Splitting: Divides data into training and testing sets with an 80-20 split.
% Model Training: Trains Kriging models for each output variable (X_samples and y_samples) using a hypothetical function train_kriging.
% Prediction Calculation: Computes predictions and error metrics.
% Plotting: Shows:
% Known y_test vs. predicted values y_pred, helping visualize the fit.
% Error curves for y_test and y_pred, with 'Prediction Error' displayed in the title.
% Objective Function: Calculates minimun -log_likelihood (i.e., maximum log_likelihood) based on the predicted values for the given data.
% Evolutionary Algorithm:
% 1)Initializes a random population (hyperparameters population).
% 2)Iterates through selection, crossover, and mutation.
% 3)Keeps best solutions after each generation.
% Kriging Model Prediction:
% Uses optimized hyperparameters to make predictions on test data.
% visualizes results.

引用格式

Chixin Xiao (2025). Prediction based on Kriging with EA-evolving Hyperparameters (https://ww2.mathworks.cn/matlabcentral/fileexchange/175878-prediction-based-on-kriging-with-ea-evolving-hyperparameters), MATLAB Central File Exchange. 检索时间: .

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