Offline Data-Driven Evolutionary Optimization Algorithm Using K-Fold Cross

Wang, Mengzhen; Shan, Yawen; Xu, Fei

doi:10.1007/978-3-031-09677-8_26

Cited by 1 publication

(2 citation statements)

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“…An essential measurement for evaluating DDEAs is approximation errors of the surrogate model, which directly determines the optimization accuracy to some degree [4]. Since an excellent surrogate model can greatly reduce the approximation error, the selection of a suitable surrogate model becomes an important part of constructing DDEAs [8]. In addition, how to make full use of the existing data to train the model is also an essential issue.…”

Section: Introductionmentioning

confidence: 99%

“…Most machine learning models and neural networks can be used as surrogates, including radial basis functions [8], polynomial response surface methods [9], Kriging methods [10,11], support vector machines [12], artificial neural networks [13], and so forth. Of these, the radial basis function(RBF) has advantages comprising fast convergence speed and strong robustness, and the approximation accuracy of neural networks (NN) is better than that of most other machine learning models.…”

Section: Introductionmentioning

confidence: 99%

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An Offline Weighted-Bagging Data-Driven Evolutionary Algorithm with Data Generation Based on Clustering

et al. 2023

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In recent years, a variety of data-driven evolutionary algorithms (DDEAs) have been proposed to solve time-consuming and computationally intensive optimization problems. DDEAs are usually divided into offline DDEAs and online DDEAs, with offline DDEAs being the most widely studied and proven to display excellent performance. However, most offline DDEAs suffer from three disadvantages. First, they require many surrogates to build a relatively accurate model, which is a process that is redundant and time-consuming. Second, when the available fitness evaluations are insufficient, their performance tends to be not entirely satisfactory. Finally, to cope with the second problem, many algorithms use data generation methods, which significantly increases the algorithm runtime. To overcome these problems, we propose a brand-new DDEA with radial basis function networks as its surrogates. First, we invented a fast data generation algorithm based on clustering to enlarge the dataset and reduce fitting errors. Then, we trained radial basis function networks and carried out adaptive design for their parameters. We then aggregated radial basis function networks using a unique model management framework and demonstrated its accuracy and stability. Finally, fitness evaluations were obtained and used for optimization. Through numerical experiments and comparisons with other algorithms, this algorithm has been proven to be an excellent DDEA that suits data optimization problems.

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Section: Introductionmentioning

confidence: 99%