Genetic Algorithm Based Deep Learning Neural Network Structure and Hyperparameter Optimization

Lee, Sanghyeop; Kim, Junyeob; Kang, Hyeon; Kang, Do‐Young; Park, Jang-Sik

doi:10.3390/app11020744

Cited by 56 publications

(23 citation statements)

References 20 publications

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“…The proposed strategy achieved an accuracy of 93.5%. A study [40] applied network architecture and hyperparameters optimization based on a Genetic Algorithm. They used an amyloid brain image dataset that contains PET/CT images of 414 patients.…”

Section: Related Studiesmentioning

confidence: 99%

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

Baghdadi

Malki

Balaha

et al. 2022

Sensors

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Alzheimer’s disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer’s disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer’s patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A3C-TL-GTO framework for MRI image classification and AD detection. The A3C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer’s Dataset (four classes of images) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer’s disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer’s Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.

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Section: Related Studiesmentioning

confidence: 99%

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

Baghdadi

Malki

Balaha

et al. 2022

Sensors

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“…Lee et al [23] applied the genetic algorithm on CNN using both network configuration and hyperparameters. They showed that their algorithm outperformed genetic CNN by 11.74% on an amyloid brain image dataset used for Alzheimer's disease diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things–23 dataset

Thavasimani¹,

Srinath²

2022

IJECE

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Hyperparameter optimization is one of the main challenges in deep learning despite its successful exploration in many areas such as image classification, speech recognition, natural language processing, and fraud detections. Hyperparameters are critical as they control the learning rate of a model and should be tuned to improve performance. Tuning the hyperparameters manually with default values is a challenging and time-intensive task. Though the time and efforts spent on tuning the hyperparameters are decreasing, it is always a burden when it comes to a new dataset or solving a new task or improving the existing model. In our paper, we propose a custom genetic algorithm to auto-tune the hyperparameters of the deep learning sequential model to classify benign and malicious traffic from internet of things-23 dataset captured by Czech Technical University, Czech Republic. The dataset is a collection of 30.85 million records of malicious and benign traffic. The experimental results show a promising outcome of 98.9% accuracy.

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“…Although ANN is used to predict the solution for the given inputs, genetic algorithms are strongly focused on the optimization of the problem. Several applications based on genetic algorithms have shown their applicability in chemical engineering [80] and biotechnology [81], among other fields.…”

Section: Machine Learning For Plastic Pyrolysismentioning

confidence: 99%

Application of computational approach in plastic pyrolysis kinetic modelling: a review

Armenise

Wong

Ramírez-Velásquez

et al. 2021

Reac Kinet Mech Cat

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During the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design.

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Genetic Algorithm Based Deep Learning Neural Network Structure and Hyperparameter Optimization

Cited by 56 publications

References 20 publications

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things–23 dataset

Application of computational approach in plastic pyrolysis kinetic modelling: a review

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