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Electronics
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Evolutionary Computation Methods for Real-World Problem Solving
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Evolutionary Computation Methods for Real-World Problem Solving

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 2339

Special Issue Editors

Dr. Zhengwen Huang

E-Mail Website
Guest Editor
College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
Interests: evolutionary algorithm; gene expression programming; machine learning; data engineering for big data analytics in smart grid; HPC; smart manufacturing
Dr. Alireza Mousavi

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Systems Engineering Research Group, Brunel University, London UB8 3PH, UK
Interests: applied control and automation; system engineering; autonomous systems and robotics; data analytics; machine learning; AI

Special Issue Information

Dear Colleagues,

This Special Issue wishes to solicit state-of-the-art research or works in progress on Evolutionary Computation Methods for Real-World Problem Solving. Potential topics include, but are not limited to, multi-objectives optimization, self-adaptive system modelling, genetic programming/gene expression programming, deep neural network models, evolutionary data engineering, machine learning with data engineering, evolutionary information/knowledge representation, evolutionary data encryptions, and evolutionary computational architecture for real-world problem solving.

We welcome original research articles covering real evolutionary computation solution applications in real-world problems, as well as methods, applications, case studies, challenges, and developments in complex system engineering areas including, but not limited to, manufacturing, transportation, telecommunications, power systems, and medical engineering.

Dr. Zhengwen Huang
Dr. Alireza Mousavi
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. Electronics is an international peer-reviewed open access semimonthly 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

  • evolutionary computation
  • evolutionary data engineering
  • evolutionary information/knowledge representation
  • evolutionary computational architecture

Published Papers (2 papers)

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Research

14 pages, 470 KiB  
Article
Genetic Algorithm for High-Dimensional Emotion Recognition from Speech Signals
by Liya Yue, Pei Hu, Shu-Chuan Chu and Jeng-Shyang Pan
Electronics 2023, 12(23), 4779; https://doi.org/10.3390/electronics12234779 - 25 Nov 2023
Viewed by 629
Abstract
Feature selection plays a crucial role in establishing an effective speech emotion recognition system. To improve recognition accuracy, people always extract as many features as possible from speech signals. However, this may reduce efficiency. We propose a hybrid filter–wrapper feature selection based on [...] Read more.
Feature selection plays a crucial role in establishing an effective speech emotion recognition system. To improve recognition accuracy, people always extract as many features as possible from speech signals. However, this may reduce efficiency. We propose a hybrid filter–wrapper feature selection based on a genetic algorithm specifically designed for high-dimensional (HGA) speech emotion recognition. The algorithm first utilizes Fisher Score and information gain to comprehensively rank acoustic features, and then these features are assigned probabilities for inclusion in subsequent operations according to their ranking. HGA improves population diversity and local search ability by modifying the initial population generation method of genetic algorithm (GA) and introducing adaptive crossover and a new mutation strategy. The proposed algorithm clearly reduces the number of selected features in four common English speech emotion datasets. It is confirmed by K-nearest neighbor and random forest classifiers that it is superior to state-of-the-art algorithms in accuracy, precision, recall, and F1-Score. Full article
(This article belongs to the Special Issue Evolutionary Computation Methods for Real-World Problem Solving)
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17 pages, 3156 KiB  
Article
Multiobjective Learning to Rank Based on the (1 + 1) Evolutionary Strategy: An Evaluation of Three Novel Pareto Optimal Methods
by Walaa N. Ismail, Osman Ali Sadek Ibrahim, Hessah A. Alsalamah and Ebtesam Mohamed
Electronics 2023, 12(17), 3724; https://doi.org/10.3390/electronics12173724 - 04 Sep 2023
Viewed by 1035
Abstract
In this research, the authors combine multiobjective evaluation metrics in the (1 + 1) evolutionary strategy with three novel methods of the Pareto optimal procedure to address the learning-to-rank (LTR) problem. From the results obtained, the Cauchy distribution as a random number generator [...] Read more.
In this research, the authors combine multiobjective evaluation metrics in the (1 + 1) evolutionary strategy with three novel methods of the Pareto optimal procedure to address the learning-to-rank (LTR) problem. From the results obtained, the Cauchy distribution as a random number generator for mutation step sizes outperformed the other distributions used. The aim of using the chosen Pareto optimal methods was to determine which method can give a better exploration–exploitation trade-off for the solution space to obtain the optimal or near-optimal solution. The best combination for that in terms of winning rate is the Cauchy distribution for mutation step sizes with method 3 of the Pareto optimal procedure. Moreover, different random number generators were evaluated and analyzed versus datasets in terms of NDCG@10 for testing data. It was found that the Levy generator is the best for both the MSLR and the MQ2007 datasets, while the Gaussian generator is the best for the MQ2008 dataset. Thus, random number generators clearly affect the performance of ES-Rank based on the dataset used. Furthermore, method 3 had the highest NDCG@10 for MQ2008 and MQ2007, while for the MSLR dataset, the highest NDCG@10 was achieved by method 2. Along with this paper, we provide a Java archive for reproducible research. Full article
(This article belongs to the Special Issue Evolutionary Computation Methods for Real-World Problem Solving)
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