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hyperparameter optimization of orthogonal functions in the numerical solution of differential equations

MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2025年第1期48卷 605-625页

作者： Aghaei, Alireza Afzal Parand, Kourosh Shahid Beheshti Univ Fac Math Sci Dept Comp & Data Sci Tehran Iran Shahid Beheshti Univ Inst Cognit & Brain Sci Dept Cognit Modeling Tehran Iran

Numerical methods for solving differential equations often rely on the expansion of the approximate solution using basis functions. The choice of an appropriate basis function plays a crucial role in enhancing the accuracy of the solution. In this study, our aim is to develop algorithms that can identify an optimal basis function for any given differential equation. To achieve this, we explore fractional rational Jacobi functions as a versatile basis, incorporating hyperparameters related to rational mappings, Jacobi polynomial parameters, and fractional components. Our research develops hyperparameter optimization algorithms, including parallel grid search, parallel random search, Bayesian optimization, and parallel genetic algorithms. To evaluate the impact of each hyperparameter on the accuracy of the solution, we analyze two benchmark problems on a semi-infinite domain: Volterra's population model and Kidder's equation. We achieve improved convergence and accuracy by judiciously constraining the ranges of the hyperparameters through a combination of random search and genetic algorithms. Notably, our findings demonstrate that the genetic algorithm consistently outperforms other approaches, yielding superior hyperparameter values that significantly enhance the quality of the solution, surpassing state-of-the-art results.

关键词： hyperparameter optimization Jacobi polynomials machine learning nonlinear differential equations

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hyperparameter optimization in Generative Adversarial Networks (GANs) Using Gaussian AHP

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IEEE ACCESS 2025年 13卷 770-788页

作者： Rodrigues, Thiago Serafim Pinheiro, Placido Rogerio Univ Fortaleza Grad Program Appl Informat BR-60811905 Fortaleza Brazil

This study explores optimizing hyperparameters in Generative Adversarial Networks (GANs) using the Gaussian Analytical Hierarchy Process (Gaussian AHP). By integrating machine learning techniques and multi-criteria decision methods, the aim is to enhance the performance and efficiency of GAN models. It trains GAN models using the Fashion MNIST dataset. It applies Gaussian AHP to optimize hyperparameters based on multiple performance criteria, such as the quality of generated images, training stability, and training time. Iterative experiments validate the methodology by automatically adjusting hyperparameters based on the obtained scores, thereby maximizing the model's efficiency and quality. Results indicate significant improvements in image generation quality and computational efficiency. The study highlights the effectiveness of combining Gaussian AHP with GANs for systematic hyperparameter optimization, providing insights into achieving higher performance in image generation tasks. Future research could extend this approach to other neural network architectures and diverse datasets, further demonstrating the versatility of this optimization technique. This method's potential applications extend across various domains, including data augmentation and anomaly detection, indicating its broad applicability and impact.

关键词： Training hyperparameter optimization Generative adversarial networks optimization Computational modeling Bayes methods Scalability Analytic hierarchy process Load modeling Iterative methods hyperparameter optimization gaussian analytical hierarchy process multicriteria decision-making machine learning

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hyperparameter optimization for Software Bug Prediction Using Ensemble Learning

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IEEE ACCESS 2024年 12卷 51869-51878页

作者： Al-Fraihat, Dimah Sharrab, Yousef Al-Ghuwairi, Abdel-Rahman Alshishani, Hamzeh Algarni, Abdulmohsen Isra Univ Fac Informat Technol Dept Software Engn Amman 11622 Jordan Isra Univ Fac Informat Technol Dept Data Sci & Artificial Intelligence Amman 11622 Jordan Hashemite Univ Fac Prince Al Hussien Bin Abdullah IT Dept Software Engn Zarqa 13133 Jordan King Khalid Univ Dept Comp Sci Abha 61421 Saudi Arabia

Software Bug Prediction (SBP) is an integral process to the software's success that involves predicting software bugs before their occurrence. Detecting software bugs early in the development process enhances software quality, performance, and reduces software costs. The integration of Machine Learning (ML) algorithms has significantly improved software bug prediction accuracy and concurrently reduced costs and resource utilization. Numerous studies have explored the impact of hyperparameter optimization on single classifiers, enhancing these models' overall performance in SBP analysis. Ensemble Learning (EL) approaches have also demonstrated increased model accuracy and performance on SBP datasets. This study proposes a novel learning model for predicting software bugs through the utilization of EL and tuning hyperparameters. The results are compared with single hypothesis learning models using the WEKA software. The dataset, collected by the National Aeronautics and Space Administration (NASA) U.S.A., comprises 10,885 instances with 20 attributes, including a classifier for defects in one of their coding projects. The findings indicate that EL models outperform single hypothesis learning models, and the proposed model's accuracy increases after optimization. Furthermore, the accuracy of the proposed model demonstrates improvement following the optimization process. These results underscore the efficacy of ensemble learning, coupled with hyperparameter optimization, as a viable approach for enhancing the predictive capabilities of software bug prediction models.

关键词： Software bug prediction machine learning hyperparameter optimization ensemble learning

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hyperparameter optimization for Large-Scale Remote Sensing Image Analysis Tasks: A Case Study Based on Permafrost Landform Detection Using Deep Learning

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IEEE ACCESS 2024年 12卷 43062-43077页

作者： Perera, Amal S. Witharana, Chandi Manos, Elias Liljedahl, Anna K. Univ Connecticut Dept Nat Resources & Environm Storrs CT USA Woodwell Climate Res Ctr Falmouth MA 02540 USA

Climate change pressure on the Arctic permafrost is rising alarmingly, creating a decisive need to produce Pan-Arctic scale permafrost landform and thaw disturbance information from remote sensing (RS) data. Very high spatial resolution (VHSR) satellite images can be utilized to detect ice-wedge polygons (IWPs)- the most important and widespread landform in the Arctic tundra region - across the Arctic without compromising spatial details. Automated analysis of peta-byte scale VHSR imagery covering millions of square kilometers is a computationally challenging task. Traditional semantic segmentation requires the use of task specific feature extraction with conventional classification techniques. Semantic complexity of VHSR images coupled with landscape heterogeneity makes it difficult to use conventional classification approaches to produce Pan-Arctic scale geospatial products. This leads to adapting deep convolutional neural network (DLCNN) approaches that have excelled in computer vision (CV) applications. Transitioning domains from everyday image understanding to remote sensing image analysis is challenging. This study aims to systematically investigate two main obstacles confronted when adapting DLCNNs in large-scale RS image analysis tasks;1) the limited availability labeled data sets and 2) the prohibitive nature of hyperparameter tunning when designing DLCNNs that can capture the rich characteristics embedded in remotely-sensed images. With a case study on the production of the first pan-Arctic ice-wedge polygon map using thousands of VHSR images, we demonstrate the use of transfer learning and the impact of hyperparameter tuning with a 16% improvement of the Mean Average Precision (mAP50).

关键词： Remote sensing deep learning hyperparameter optimization terrain mapping convolutional neural networks climate change environmental monitoring Arctic tundra mask R-CNN

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hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain

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JOURNAL OF HYDROLOGY 2024年 643卷

作者： Hosseini, F. Prieto, C. Alvarez, C. Univ Cantabria Inst Hidraul Ambiental Santander Spain

This paper introduces a novel approach for hyperparameter optimization of long short-term memory networks (LSTMs) to achieve highly accurate hourly streamflow and water level predictions in the realm of regional rainfall-runoff modeling. Leveraging simultaneous systematic hyperparameter optimization of 10 distinct hyperparameters by Random Search, the study achieves high accuracy in terms of predictions across 40 humid flashy catchments in Basque Country, north of Spain. By carefully designing the search space and incorporating domain expertise, the approach quickly converges to optimal and highly accurate network configurations with both efficiency and efficacy. LSTMs ingested precipitation, temperature, and potential evapotranspiration as inputs to predict 2 targets of streamflow and water level, in an hourly timestep. On the test set, the optimized LSTM networks accurately predicted streamflow and water level with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) efficiencies as high as 0.97, in one of the catchments. Across all 40 studied catchments, the overall average NSE and KGE values for streamflow were 0.89 and 0.87, respectively;water level exhibited average NSE and KGE scores of 0.91 and 0.92. Moreover, statistical analysis reveals significant differences in the performance of the 2 distinct optimized network architectures in different hydrological catchments, underscoring the importance of deliberate network configuration selection post-random search. This selection process is vital for achieving higher performance in as many catchments as possible. The findings highlight opportunities for enhancing the "learning maturity" of regional hydrological deep learning LSTM networks. This research provides valuable insights for researchers and practitioners involved in optimizing regional hydrological deep learning models for a variety of applications and on new datasets.

关键词： Hydrological modeling LSTM networks hyperparameter optimization Random search Hourly prediction

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hyperparameter optimization and Importance Ranking in Deep Learning-Based Crack Segmentation

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2024年第2期38卷 04023042-04023042页

作者： Canchila, Carlos Zhou, Shanglian Song, Wei Univ Alabama Dept Civil Construct & Environm Engn Tuscaloosa AL 35487 USA Univ Hawaii Manoa Dept Civil & Environm Engn Honolulu HI 96822 USA

Although deep convolutional neural networks (DCNNs) have been widely adopted for crack segmentation, they often demonstrate performance degradation on data with real-world complexities. To achieve consistent and accurate prediction performance with complex and feature-rich real-world data, DCNN hyperparameters must be properly selected or optimized. The goal of this study is to provide a novel hyperparameter optimization framework for future crack segmentation DCNN designs to follow, and gain insights into hyperparameter importance on segmentation performance. In this study, a Bayesian optimization framework and an accompanying global sensitivity analysis have been proposed to guide the search for optimal crack segmentation DCNNs using real-world 3D roadway range images. The proposed Bayesian optimization framework can determine the optimal configurations for both training- and architecture-related hyperparameters. In addition, the probabilistic models developed during Bayesian optimization are leveraged by the accompanying global sensitivity analysis to interpret and rank the hyperparameter importance on DCNNs' segmentation accuracy.

关键词： Crack segmentation Deep convolutional neural networks (DCNNs) hyperparameter optimization Bayesian optimization Gaussian process Kullback-Leibler divergence Global sensitivity analysis Sobol analysis Importance ranking

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hyperparameter optimization of pre-trained convolutional neural networks using adolescent identity search algorithm

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NEURAL COMPUTING & APPLICATIONS 2024年第4期36卷 1523-1537页

作者： Akkus, Ebubekir Bal, Ufuk Kocoglu, Fatma Oenay Beyhan, Selami Aydin Adnan Menderes Univ Aydin Vocat Sch Dept Elect & Automat Aydin Turkiye Osmaniye Korkut Ata Univ Fac Engn Elect & Elect Engn Osmaniye Turkiye Mugla Sitki Kocman Univ Fac Engn Software Engn Mugla Turkiye Izmir Democracy Univ Fac Engn Elect & Elect Engn Izmir Turkiye

Convolutional neural networks (CNNs) are widely used deep learning (DL) models for image classification. The selected hyperparameters for training convolutional neural network (CNN) models have a significant effect on the performance. Therefore, hyperparameter optimization (HPO) is an important process to design optimal CNN models. In this study, Adolescent Identity Search Algorithm (AISA) and Bayesian optimization (BO) methods were applied for HPO of pre-trained CNN models to improve their classification performance. Diabetic retinopathy (DR) classification was chosen as the application problem of the study and Kaggle Diabetic Retinopathy Detection dataset was used. We used pre-trained CNN models named AlexNet, MobileNetV2, ResNet18, and GoogLeNet. To the best of our knowledge, this study represents the first use of AISA-based HPO for DR classification. The results show that hybrid models incorporating AISA-based HPO achieve better accuracy with fewer iterations than BO-based HPO hybridized models.

关键词： hyperparameter optimization Diabetic retinopathy Transfer learning Bayesian optimization AISA optimization

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hyperparameter optimization of neural networks based on Q-learning

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第4期17卷 1669-1676页

作者： Qi, Xin Xu, Bing Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hung Hom Hong Kong Peoples R China

Machine learning algorithms are sensitive to hyperparameters, and hyperparameter optimization techniques are often computationally expensive, especially for complex deep neural networks. In this paper, we use Q-learning algorithm to search for good hyperparameter configurations for neural networks, where the learning agent searches for the optimal hyperparameter configuration by continuously updating the Q-table to optimize hyperparameter tuning strategy. We modify the initial states and termination conditions of Q-learning to improve search efficiency. The experimental results on hyperparameter optimization of a convolutional neural network and a bidirectional long short-term memory network show that our method has higher search efficiency compared with tree of Parzen estimators, random search and genetic algorithm and can find out the optimal or near-optimal hyperparameter configuration of neural network models with minimum number of trials.

关键词： hyperparameter optimization Q-learning Neural networks Markov decision process

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hyperparameter optimization of Long Short Term Memory Models for Interpretable Electrical Fault Classification

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IEEE ACCESS 2023年 11卷 123688-123704页

作者： Biju, G. M. Pillai, G. N. Indian Inst Technol Roorkee Dept Elect Engn Roorkee 247667 India

The reliability of the model significantly affects early detection and accurate classification of electrical faults. In this study, a Long Short Term Memory based fault classification model was developed for the Power System Machine Learning benchmark dataset, focusing on improving reliability by increasing interpretability. First, novel metrics are introduced to measure model interpretability. These interpretability metrics are uniquely defined based on the disentanglement of the fault classification factors. Subsequently, hyperparameter optimization was performed using multi-objective Bayesian optimization to determine the optimal model architecture. The objective of optimization is to maximize interpretability and classification accuracy. The Pareto-optimal solution presents different model architectures with varying accuracy and interpretability trade-offs. Finally, the manifestation of interpretability in terms of subsequences is studied using Shapley Additive Explanations. The impact of class representation and architectural parameters on interpretability was also analyzed. Furthermore, the most accurate model in the Pareto front achieved highly competitive accuracy for the benchmark data.

关键词： hyperparameter optimization Long short term memory Artificial intelligence Fault detection optimization methods Pareto optimization Power grids Power system reliability Recurrent neural networks Time series analysis deep learning electrical fault detection hyperparameter optimization interpretability long short term memory pareto optimization power grids power system reliability recurrent neural networks time series analysis

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hyperparameter optimization for AST Differencing

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第10期49卷 4814-4828页

作者： Martinez, Matias Falleri, Jean-Remy Monperrus, Martin Univ Politecn Cataluna Barcelona 08034 Spain Univ Bor deaux CNRS Bordeaux INP LaBRIUMR 5800 F-33400 Deaux France Inst Univ France Paris France KTH Royal Inst Technol S-11428 Stockholm Sweden

Computing the differences between two versions of the same program is an essential task for software development and software evolution research. AST differencing is the most advanced way of doing so, and an active research area. Yet, AST differencing algorithms rely on configuration parameters that may have a strong impact on their effectiveness. In this paper, we present a novel approach named DAT (Diff Auto Tuning) for hyperparameter optimization of AST differencing. We thoroughly state the problem of hyper-configuration for AST differencing. We evaluate our data-driven approach DAT to optimize the edit-scripts generated by the state-of-the-art AST differencing algorithm named GumTree in different scenarios. DAT is able to find a new configuration for GumTree that improves the edit-scripts in 21.8% of the evaluated cases.

关键词： Training Software algorithms Computer bugs Syntactics Maintenance engineering hyperparameter optimization Software Software evolution Tree differencing Abstract Syntax Trees (AST) hyperparameter optimization, edit-script

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