Genetic Algorithm-based Optimization of Deep Neural Network Ensemble

Feng, Xuanang; Zhao, Jianing; Kita, Eisuke

doi:10.1007/s12626-021-00074-9

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…Feng and other researchers proposed a network structure based on genetic algorithm, which generates a basic model dominated by an integrated algorithm by adjusting parameters and takes the original personal identification data as the numerical value. The results show that the prediction accuracy of the integrated model is better than the basic model, making personal identification occupy an important position in biological feature recognition [7]. Nikbakht's team applied the genetic algorithm to the neural network to find the optimal solution and optimized the hidden layer, integration point, and neuron number of each layer to achieve the highest accuracy, so as to predict the stress distribution of the structure.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Kuang

2022

Journal of Function Spaces

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With the continuous development of urbanization, the urban population is becoming more and more dense, and the demand for land is becoming more and more tense. Urban expansion has become an indispensable part of urban development. This paper studies the optimization of neural network structure by genetic algorithm, puts forward the prediction model of urban scale expansion based on a genetic algorithm optimization neural network, and compares the performance of the model with the basic model. A genetic algorithm BP neural network (GA-BP) optimized by the genetic algorithm is used to shorten the running time of the algorithm and improve the prediction accuracy, but it is easy to fall into local solution. The genetic algorithm is improved by immune cloning algorithm, and the CGA-BP neural network model is established to obtain the global optimal solution. Compared with the BP neural network model and GA-BP neural network model, the CGA-BP neural network model converges faster, and the training times reach the error condition after 79 times, while the BP neural network model and GA-BP neural network model need 117 times and 100 times, respectively, and the fitness value corresponding to the number of iterations of the model is larger. Therefore, the CGA-BP neural network algorithm can make prediction more accurately and quickly and predict the expansion of urban scale through urban conditions.

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Section: Related Workmentioning

confidence: 99%

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Kuang

2022

Journal of Function Spaces

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“…It’s crucial to note that, in order to guarantee the predictive performance of the ensemble learning model, the selection of basic learners must consider both model accuracy as well as model diversity. GA [ 14 , 31 ] mimics the natural process of chromosomal recombination evolution and has been demonstrated to be well-suited for optimization problems related to genomics. To streamline and automate this process, we utilize the “GA” package to optimize the selected basic learners.…”

Section: The Details Of Machine Learning Methods Of Basic Learnersmentioning

confidence: 99%

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

Sun,

Nong,

Meng

et al. 2024

J Transl Med

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Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

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“…For example, Ayan et al [36] used a GA to perform a weighted integration of three CNN for crop pest classification tasks. Feng et al [37] applied GAs to construct a CNN structure and optimize the hyperparameter settings of the model.…”

Section: Gasmentioning

confidence: 99%

GA-based weighted ensemble learning for multi-label aerial image classification using convolutional neural networks and vision transformers

Tseng

2023

Mach. Learn.: Sci. Technol.

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Multi-label classification (MLC) of aerial images is a crucial task in remote sensing image analysis. Traditional image classification methods have limitations in image feature extraction, leading to an increasing use of deep learning models, such as convolutional neural networks (CNN) and vision transformers (ViT). However, the standalone use of these models may have limitations when dealing with MLC. To enhance the generalization performance of MLC of aerial images, this paper combines two CNN and two ViT models, comparing four single deep learning models, a manually weighted ensemble learning method, and a GA-based weighted ensemble method. The experimental results using two public multi-label aerial image datasets show that the classification performance of ViT models is better than CNN models, the traditional weighted ensemble learning model performs better than a single deep learning model, and the GA-based weighted ensemble method performs better than the manually weighted ensemble learning method. The GA-based weighted ensemble method proposed in this study can achieve better MLC performance of aerial images than previous results.

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Genetic Algorithm-based Optimization of Deep Neural Network Ensemble

Cited by 13 publications

References 15 publications

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

GA-based weighted ensemble learning for multi-label aerial image classification using convolutional neural networks and vision transformers

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