Computational Intelligence and Neuroscience

Neuroevolution: Methods and Applications


Publishing date
01 Sep 2022
Status
Closed
Submission deadline
13 May 2022

Lead Editor

1Universidad de Guadalajara, Guadalajara, Mexico

2Hakim Sabzevari University, Sabzevari, Iran

3Loughborough University, Loughborough, UK

4University of New South Wales Canberra, Canberra, Australia

This issue is now closed for submissions.
More articles will be published in the near future.

Neuroevolution: Methods and Applications

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Neuroevolution (NE) refers to the emerging techniques which combine the search ability of evolutionary computation (EC) and the learning capability of artificial neural networks (ANN) for various tasks, such as finding parameters, hyperparameters and architecture in a typical ANN.

Neuroevolution can be used in all arbitrary neural models and network architectures and in all methods in evolutionary computation to address challenging problems in a sizeable range of domains such as reinforcement learning, supervised learning, unsupervised learning, computer vision, text mining, and speech processing. There are two main methods to combine EC and ANN, first is when we use EC combined with ANN, any parameters, hyper-parameters, and architecture can be optimized by using EC. It not only can use the powerful search ability of EC but also use custom objective function, for instance, the complexity of the model can be minimized, which is hard using conventional optimization algorithms. The second method to combine EC and ANN is when we use ANN combined with EC, the evaluated candidate solutions can be considered as a dataset so that it can estimate the problem model and the fitness landscape, which is essential in computationally expensive problems.

This Special Issue welcomes original research and review articles on the topic of neuroevolution and the combination of EC with ANN.

Potential topics include but are not limited to the following:

  • Evolutionary computation algorithms, such as enetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), genetic programming (GP), ant colony optimization (ACO) in combination with ANNs
  • Neural networks, such as convolutional neural networks (CNN), long short-term memory (LSTM), deep belief network (DBN), feedforward neural networks (FNN), recurrent neural networks (RNN), radial basisfunction neural networks (RBF) in combination with EC
  • Novel algorithms for learning the weights of an ANN, and finding the proper hyperparameters
  • Evolutionary neural architecture
  • Novel search mechanisms
  • Surrogate assisted neuroevolution
  • Novel representations and objective functions
  • Hybrid EC/ANN approaches
  • Multi-objective neuroevolution
  • Analysis of the complexity of neuroevolution
  • Landscape analysis by ANN for EC
  • Application of neuroevolution in other scientific fields including image processing and computer vision, text mining and natural language processing, speech processing, software engineering, time series analysis, healthcare, cybersecurity, finance and fraud detection, social networks, recommender systems, and evolutionary robotics

Articles

  • Special Issue
  • - Volume 2022
  • - Article ID 9240843
  • - Research Article

Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction

Hualong Bu | Jing Xia | ... | Liping Chen
  • Special Issue
  • - Volume 2022
  • - Article ID 4027667
  • - Research Article

BDS-3 Broadcast Ephemeris Orbit Correction Model Based on Improved PSO Combined with BP Neural Network

Jiebo Peng | Feng Liu | Wenjin Hu
  • Special Issue
  • - Volume 2022
  • - Article ID 1334081
  • - Research Article

A Multiobjective Optimization Model for a Dynamic and Sustainable Cellular Manufacturing System under Uncertainty

Javad Jafarzadeh | Hossein Amoozad Khalili | Naghi Shoja
  • Special Issue
  • - Volume 2022
  • - Article ID 9374946
  • - Research Article

Recall Network: A Simple Brain-Inspired Algorithm for Classification

Zhaoning Tian | Ying Li | ... | Site Li
  • Special Issue
  • - Volume 2022
  • - Article ID 1482250
  • - Research Article

Research on Clustering Algorithm Based on Improved SOM Neural Network

Chengxiang Shi | Xiaoqing Li
  • Special Issue
  • - Volume 2022
  • - Article ID 9541115
  • - Research Article

Jellyfish Search-Optimized Deep Learning for Compressive Strength Prediction in Images of Ready-Mixed Concrete

Jui-Sheng Chou | Stela Tjandrakusuma | Chi-Yun Liu
  • Special Issue
  • - Volume 2022
  • - Article ID 1279945
  • - Research Article

Leveraging a Neuroevolutionary Approach for Classifying Violent Behavior in Video

Carlos Flores-Munguía | José C. Ortiz-Bayliss | Hugo Terashima-Marín
  • Special Issue
  • - Volume 2022
  • - Article ID 2340856
  • - Research Article

The intelligent Traffic Management System for Emergency Medical Service Station Location and Allocation of Ambulances

Ezzatollah Asgharizadeh | Mahsa Kadivar | ... | Adel Pourghader Chobar
  • Special Issue
  • - Volume 2022
  • - Article ID 6822467
  • - Research Article

Modeling and Estimation of CO2 Emissions in China Based on Artificial Intelligence

Pan Wang | Yangyang Zhong | Zhenan Yao
  • Special Issue
  • - Volume 2022
  • - Article ID 3082933
  • - Research Article

Application of Improved Manta Ray Foraging Optimization Algorithm in Coverage Optimization of Wireless Sensor Networks

Fang Zhu | Wenhao Wang | Shan Li
Computational Intelligence and Neuroscience
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Acceptance rate51%
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Acceptance to publication25 days
CiteScore3.900
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Impact Factor3.120
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