Evolutionary design of neural network architectures: a review of three decades of research

Ünal, Hamit Taner; Başçiftçi, Fatih

doi:10.1007/s10462-021-10049-5

Cited by 33 publications

(11 citation statements)

References 214 publications

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“…An interesting idea in this context is the idea of evaluating, whether for a given instance of data and for a given model, there is a chance to find a satisfactory solution before starting the calculations. For this purpose, the authors proposed the use of ANN (Section 6) for the classification of input data, bearing in mind that the accuracy of classification in ANN depends to a large extent on its architecture [18,19]. In the proposed framework, ANN is responsible for obtaining an evaluation of a potential solution to the problem before running the solver [20].…”

Section: Discussionmentioning

confidence: 99%

Application of Enterprise Architecture and Artificial Neural Networks to Optimize the Production Process

2023

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Production optimization is a complex process because it must take into account various resources of the company and its environment. In this process, it is necessary to consider the enterprise as a whole, taking into account the interaction between its key elements, both in the technological and business layer. For this reason, the article proposes the use of enterprise architecture, which facilitates the interaction of these layers in the production optimization process. As a result, a proprietary meta-model of enterprise architecture was presented, which, based on good practices and the assumptions of enterprise architecture, facilitates the construction of detailed optimization models in the area of planning, scheduling, resource allocation, and routing. The production optimization model formulated as a mathematical programming problem is also presented. The model was built taking into account the meta-model. Due to the computational complexity of the optimization model, a method using an artificial neural network (ANN) was proposed to estimate the potential result based on the structure of the model and a given data instance before the start of optimization. The practical application of the presented approach has been shown based on the example of optimization of the production of an exemplary production cell where the cost of storage and the number of unfulfilled orders and maintenance are optimized.

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Section: Discussionmentioning

confidence: 99%

Application of Enterprise Architecture and Artificial Neural Networks to Optimize the Production Process

2023

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“…Deep neural networks have demonstrated outstanding performance in a wide range of machine learning tasks, including classification and clustering [ 39 , 40 ], for real-life applications of soft computing techniques in different fields [ 41 , 42 ]. Developing an appropriate architecture for a Deep Convolutional Neural Network (DCNN) has remained an extremely intriguing, demanding, and topical issue to date.…”

Section: Discussionmentioning

confidence: 99%

Topology optimization search of deep convolution neural networks for CT and X-ray image classification

Louati

Bechikh

et al. 2022

BMC Med Imaging

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Covid-19 is a disease that can lead to pneumonia, respiratory syndrome, septic shock, multiple organ failure, and death. This pandemic is viewed as a critical component of the fight against an enormous threat to the human population. Deep convolutional neural networks have recently proved their ability to perform well in classification and dimension reduction tasks. Selecting hyper-parameters is critical for these networks. This is because the search space expands exponentially in size as the number of layers increases. All existing approaches utilize a pre-trained or designed architecture as an input. None of them takes design and pruning into account throughout the process. In fact, there exists a convolutional topology for any architecture, and each block of a CNN corresponds to an optimization problem with a large search space. However, there are no guidelines for designing a specific architecture for a specific purpose; thus, such design is highly subjective and heavily reliant on data scientists’ knowledge and expertise. Motivated by this observation, we propose a topology optimization method for designing a convolutional neural network capable of classifying radiography images and detecting probable chest anomalies and infections, including COVID-19. Our method has been validated in a number of comparative studies against relevant state-of-the-art architectures.

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“…On the other hand, a variety of significant progresses concerning NAS and hyper‐parameters optimisation (HPO) for deep neural networks have been made in recent years [11–18]. For example, Xie et al.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, a variety of significant progresses concerning NAS and hyper-parameters optimisation (HPO) for deep neural networks have been made in recent years [11][12][13][14][15][16][17][18]. For example, Xie et al [14] employed a GA to optimise the network structure encoded in a fixed-length binary string, and the experimental results on image classification datasets, including MNIST, CIFAR10 and ILSVRC2012, are promising.…”

Section: Introductionmentioning

confidence: 99%

SOPA‐GA‐CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet‐of‐Things

Huang

Zeng

Geng

et al. 2023

IET Cyber-Syst and Robotics

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In recent years, deep learning has been applied to a variety of scenarios in Industrial Internet of Things (IIoT), including enhancing the security of IIoT. However, the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the designers. The authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT. A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks (SOPA-GA-CNN) is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks (CNNs) by genetic algorithms (GA) for the intrusion detection issue of IIoT. An efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters, including batch size, learning rate, weight optimiser and weight regularisation, and the architectures, such as the block-based network topology and the parameters of each CNN block. The experimental results on five intrusion detection datasets in IIoT, including secure water treatment, water distribution, Gas Pipeline, Botnet in Internet of Things and Power System Attack Dataset, have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy, precision, recall, F1score, and the number of parameters of the deep learning models.

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Evolutionary design of neural network architectures: a review of three decades of research

Cited by 33 publications

References 214 publications

Application of Enterprise Architecture and Artificial Neural Networks to Optimize the Production Process

Application of Enterprise Architecture and Artificial Neural Networks to Optimize the Production Process

Topology optimization search of deep convolution neural networks for CT and X-ray image classification

SOPA‐GA‐CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet‐of‐Things

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