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Symmetry
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Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its...
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Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its Applications in Big Data Analytics

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 12058

Special Issue Editors

Prof. Dr. Lianbo Ma

E-Mail Website
Guest Editor
College of Software, Northeastern University, Shenyang 110819, China
Interests: evolutionary machine learning; intelligent optimization; data processing and analytics; image and video processing
Special Issues, Collections and Topics in MDPI journals
Dr. Shi Cheng

E-Mail Website
Guest Editor
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
Interests: swarm intelligence; evolutionary algorithms; big data analytics; particle swarm optimization; brain storm optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shangce Gao

E-Mail Website
Guest Editor
Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
Interests: artificial intelligence; computational intelligence; neural networks
Special Issues, Collections and Topics in MDPI journals
Dr. Yu Guo

E-Mail Website
Guest Editor
The Key Laboratory of Smart Manufac-turing in Energy Chemical Process, East China University of Science and Technology, Shanghai 200237, China
Interests: multi-objective optimization; preference-based evolutionary algorithms; evolutionary machine learning; federated learning; industrial data processing

Special Issue Information

Dear Colleagues,

Machine learning (ML) has been widely applied for big data processing and analytics, where various optimization problems (about model symmetry/asymmetry, model architecture and hyperparameters, data clustering, and data prediction) are frequently encountered. The automatic design of machine learning has become an increasingly popular research trend. Evolutionary computation (EC) is commonly used in these scenarios where classical numerical methods fail to find good enough solutions. Evolutionary approaches can be used in all the parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting and network topology), and postprocessing (e.g., decision tree/support vectors pruning and ensemble learning). It is of great interest to investigate the combination of EC and ML in solving large-scale big data analytic problems.

The interdisciplinary research of this topic focuses on the progress of machine learning, evolutionary algorithms and their applications for big data, as well as emerging intelligent applications and models in topics of interest, including, but not limited to, industrial control, job-shop scheduling, expert systems, pattern recognition, and computer vision.

This Special Issue aims to bring together both experts and newcomers from either academia or industry to discuss new and existing issues concerning evolutionary machine learning and big data, in particular, the integration between academic research and industry applications, and to stimulate further engagement with the user community. With this Special Issue, we want to disseminate knowledge among researchers, designers, and users in this exciting field.

Prof. Dr. Lianbo Ma
Prof. Dr. Shi Cheng
Prof. Dr. Shangce Gao
Dr. Yu Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • evolutionary computation
  • multi-objective optimization
  • big data processing
  • deep learning models
  • neural architecture search
  • intelligent systems
  • industrial applications

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Published Papers (5 papers)

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Research

19 pages, 440 KiB  
Article
On Statistical Modeling Using a New Version of the Flexible Weibull Model: Bayesian, Maximum Likelihood Estimates, and Distributional Properties with Applications in the Actuarial and Engineering Fields
by Walid Emam
Symmetry 2023, 15(2), 560; https://doi.org/10.3390/sym15020560 - 20 Feb 2023
Cited by 5 | Viewed by 1800
Abstract
In this article, we present a new statistical modification of the Weibull model for updating the flexibility, called the generalized Weibull-Weibull distribution. The new modification of the Weibull model is defined and studied in detail. Some mathematical and statistical functions are studied, such [...] Read more.
In this article, we present a new statistical modification of the Weibull model for updating the flexibility, called the generalized Weibull-Weibull distribution. The new modification of the Weibull model is defined and studied in detail. Some mathematical and statistical functions are studied, such as the quantile function, moments, the information generating measure, the Shannon entropy and the information energy. The joint distribution functions of the two marginal univariate models via the Copula model are provided. The unknown parameters are estimated using the maximum likelihood method and Bayesian method via Monte Carlo simulations. The Bayesian approach is discussed using three different loss functions: the quadratic error loss function, the LINEX loss function, and the general entropy loss function. We perform some numerical simulations to show how interesting the theoretical results are. Finally, the practical application of the proposed model is illustrated by analyzing two applications in the actuarial and engineering fields using corporate data to show the elasticity and advantage of the proposed generalized Weibull-Weibull model. The practical applications show that proposed model is very suitable for modeling actuarial and technical data sets and other related fields. Full article
(This article belongs to the Special Issue Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its Applications in Big Data Analytics)
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19 pages, 4133 KiB  
Article
Modeling the Amount of Carbon Dioxide Emissions Application: New Modified Alpha Power Weibull-X Family of Distributions
by Walid Emam and Yusra Tashkandy
Symmetry 2023, 15(2), 366; https://doi.org/10.3390/sym15020366 - 30 Jan 2023
Cited by 6 | Viewed by 1609
Abstract
The use of statistical distributions to model life phenomena has received considerable attention in the literature. Recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in environmental sciences. Among them, the Weibull distribution is one of [...] Read more.
The use of statistical distributions to model life phenomena has received considerable attention in the literature. Recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in environmental sciences. Among them, the Weibull distribution is one of the most well-known models that can be used very effectively for modeling data in the fields of pollution and gas emissions, to name a few. In this paper, we introduce a family of distributions, which we call the modified Alpha-Power Weibull-X family of distributions. Based on the proposed family, we introduce a new model with five parameters, the modified Alpha-Power Weibull–Weibull distribution. Some mathematical properties were determined. Bayesian and maximum likelihood estimates for the model parameters were derived. The MLEs, bootstrap and Bayesian HPD credibility intervals for the unknown parameters were performed. A Monte Carlo simulation study was performed to evaluate the performance of the estimates. A simulation study was performed based on the parameters of the proposed model. An application to the carbon dioxide emissions dataset was performed to predict unique symmetric and asymmetric patterns and illustrate the applicability and potential of the model. For this data set, the proposed model is compared with the modified alpha power Weibull exponential distribution and the two-parameter Weibull distribution. To show which of the competing distributions is the best, we draw on certain analytical tools such as the Kolmogorov–Smirnov test. Based on these analytical measures, we found that the new model outperforms the competing models. Full article
(This article belongs to the Special Issue Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its Applications in Big Data Analytics)
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19 pages, 308 KiB  
Article
The Arcsine Kumaraswamy-Generalized Family: Bayesian and Classical Estimates and Application
by Walid Emam and Yusra Tashkandy
Symmetry 2022, 14(11), 2311; https://doi.org/10.3390/sym14112311 - 3 Nov 2022
Cited by 6 | Viewed by 1543
Abstract
In this paper, by including a trigonometric function, we propose a family of heavy-tailed distribution called the arcsine Kumaraswamy generalized-X family of distributions. Based on the proposed approach, a four-parameter extension of the Lomax distribution called the arcsine Kumaraswamy generalized Lomax (ASKUG-LOMAX) distribution [...] Read more.
In this paper, by including a trigonometric function, we propose a family of heavy-tailed distribution called the arcsine Kumaraswamy generalized-X family of distributions. Based on the proposed approach, a four-parameter extension of the Lomax distribution called the arcsine Kumaraswamy generalized Lomax (ASKUG-LOMAX) distribution is discussed in detail. Maximum likelihood, bootstrap, and Bayesian estimation are used to estimate the model parameters. A simulation study is used to evaluate ASKUG-LOMAX performance. The flexibility and usefulness of the proposed ASKUG-LOMAX distribution to predict unique symmetric and asymmetric patterns is demonstrated by analyzing real data. The results show that the ASKUG-LOMAX model is a good candidate for analyzing claims based on heavy-tailed data. Full article
(This article belongs to the Special Issue Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its Applications in Big Data Analytics)
30 pages, 4510 KiB  
Article
An Improved Equilibrium Optimizer with a Decreasing Equilibrium Pool
by Lin Yang, Zhe Xu, Yanting Liu and Guozhong Tian
Symmetry 2022, 14(6), 1227; https://doi.org/10.3390/sym14061227 - 13 Jun 2022
Cited by 4 | Viewed by 2170
Abstract
Big Data is impacting and changing the way we live, and its core lies in the use of machine learning to extract valuable information from huge amounts of data. Optimization problems are a common problem in many steps of machine learning. In the [...] Read more.
Big Data is impacting and changing the way we live, and its core lies in the use of machine learning to extract valuable information from huge amounts of data. Optimization problems are a common problem in many steps of machine learning. In the face of complex optimization problems, evolutionary computation has shown advantages over traditional methods. Therefore, many researchers are working on improving the performance of algorithms for solving various optimization problems in machine learning. The equilibrium optimizer (EO) is a member of evolutionary computation and is inspired by the mass balance model in environmental engineering. Using particles and their concentrations as search agents, it simulates the process of finding equilibrium states for optimization. In this paper, we propose an improved equilibrium optimizer (IEO) based on a decreasing equilibrium pool. IEO provides more sources of information for particle updates and maintains a higher population diversity. It can discard some exploration in later stages to enhance exploitation, thus achieving a better search balance. The performance of IEO is verified using 29 benchmark functions from IEEE CEC2017, a dynamic economic dispatch problem, a spacecraft trajectory optimization problem, and an artificial neural network model training problem. In addition, the changes in population diversity and computational complexity brought by the proposed method are analyzed. Full article
(This article belongs to the Special Issue Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its Applications in Big Data Analytics)
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35 pages, 3151 KiB  
Article
Intelligent Dendritic Neural Model for Classification Problems
by Weixiang Xu, Dongbao Jia, Zhaoman Zhong, Cunhua Li and Zhongxun Xu
Symmetry 2022, 14(1), 11; https://doi.org/10.3390/sym14010011 - 22 Dec 2021
Cited by 4 | Viewed by 2455
Abstract
In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into [...] Read more.
In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi’s method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed. Full article
(This article belongs to the Special Issue Symmetric Machine Learning Method Enhanced by Evolutionary Computation and Its Applications in Big Data Analytics)
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