A Review on Convolutional Neural Network Encodings for Neuroevolution

Vargas-Hákim, Gustavo A.; Mezura-Montes, Efrén; Acosta-Mesa, Héctor-Gabriel

doi:10.1109/tevc.2021.3088631

Cited by 34 publications

(15 citation statements)

References 158 publications

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“…Designing a high‐performance and efficient CNN model for a specific application scenario is a complex process because a variety of hyperparameters and architecture parameters in the CNN model should be fine‐tuned and optimised. The encoding strategy of CNNs will determine the computational efficiency and the optimisation performance of neuroevolution [43]. Inspired by the success of DenseNet and the NAS works in the image field, the model parameters could be divided into three important aspects: training parameters, network topology, and each CNN block's parameters in the model.…”

Section: The Proposed Methodsmentioning

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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Section: The Proposed Methodsmentioning

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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“…EAs can use flexible encoding for NAS. The encoding space can indicate various complex structures [40,41]. EAs can guide the exploration and determine the search direction by simple genetic operators and survival of the fittest in natural selection.…”

Section: Evolutionary Nas Frameworkmentioning

confidence: 99%

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Liu,

Li,

Zhao

et al. 2024

CMES

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Most of the neural network architectures are based on human experience, which requires a long and tedious trial-and-error process. Neural architecture search (NAS) attempts to detect effective architectures without human intervention. Evolutionary algorithms (EAs) for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures. Using multiobjective EAs for NAS, optimal neural architectures that meet various performance criteria can be explored and discovered efficiently. Furthermore, hardware-accelerated NAS methods can improve the efficiency of the NAS. While existing reviews have mainly focused on different strategies to complete NAS, a few studies have explored the use of EAs for NAS. In this paper, we summarize and explore the use of EAs for NAS, as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods. NAS performs well in healthcare applications, such as medical image analysis, classification of disease diagnosis, and health monitoring. EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task. Deep neural network has been successfully used in healthcare, but it lacks interpretability. Medical data is highly sensitive, and privacy leaks are frequently reported in the healthcare industry. To solve these problems, in healthcare, we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection. Moreover, we also point out future research directions for evolutionary NAS. Overall, for researchers who want to use EAs to optimize NNs in healthcare, we analyze the advantages and disadvantages of doing so to provide detailed guidance, and propose an interpretable privacy-preserving framework for healthcare applications.

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“…Currently, there are several variants of ANN, such as convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), etc. [ 77 , 78 , 79 , 80 , 81 ]. Each of these networks is applicable for some particular fields and categories.…”

Section: Performance Predictionmentioning

confidence: 99%

A Review of Performance Prediction Based on Machine Learning in Materials Science

Liu

Huang

et al. 2022

Nanomaterials

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With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area.

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A Review on Convolutional Neural Network Encodings for Neuroevolution

Cited by 34 publications

References 158 publications

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

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

Evolutionary Neural Architecture Search and Its Applications in Healthcare

A Review of Performance Prediction Based on Machine Learning in Materials Science

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