Trends in Evolutionary Computation with Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 486

Special Issue Editor


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Guest Editor
TecNM, Tijuana Institute of Technology, Tijuana, Mexico
Interests: computational intelligence; natural computing; fuzzy logic; evolutionary algorithms; soft computing

Special Issue Information

Dear Colleagues,

This Special Issue collects high-quality original research papers on the relevant applications of evolutionary computation (EC) techniques. Its primary focus is on contributions that showcase the practical implementation of EC algorithms for solving complex, real-world problems, exploring theoretical advancements, and proposing novel hybrid approaches.

We encourage case studies demonstrating the effectiveness of algorithms such as Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and other EC and bio-inspired techniques in engineering, healthcare, and environmental science.

We are also interested in works that combine EC algorithms with other computational methods to tackle single- or multi-objective optimization problems. Additionally, contributions discussing the implementation of nature-inspired algorithms in distributed computing environments are highly valued. Topics of interest include parallel processing, cloud-based deployments, and the scalability of distributed bio-inspired algorithms.

Dr. Mario Garcia-Valdez
Guest Editor

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Keywords

  • evolutionary computation
  • nature-inspired algorithms
  • swarm intelligence
  • bio-inspired computing
  • hybrid optimization
  • nature-inspired algorithms
  • parallel processing
  • cloud-based deployments
  • fuzzy logic
  • soft computing

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Published Papers (1 paper)

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Research

18 pages, 4817 KiB  
Article
Enhancing Wildfire Detection via Trend Estimation Under Auto-Regression Errors
by Xiyuan Liu, Lingxiao Wang, Jiahao Li, Khan Raqib Mahmud and Shuo Pang
Mathematics 2025, 13(7), 1046; https://doi.org/10.3390/math13071046 - 24 Mar 2025
Viewed by 225
Abstract
In recent years, global weather changes have underscored the importance of wildfire detection, particularly through Uncrewed Aircraft System (UAS)-based smoke detection using Deep Learning (DL) approaches. Among these, object detection algorithms like You Only Look Once version 7 (YOLOv7) have gained significant popularity [...] Read more.
In recent years, global weather changes have underscored the importance of wildfire detection, particularly through Uncrewed Aircraft System (UAS)-based smoke detection using Deep Learning (DL) approaches. Among these, object detection algorithms like You Only Look Once version 7 (YOLOv7) have gained significant popularity due to their efficiency in identifying objects within images. However, these algorithms face limitations when applied to video feeds, as they treat each frame as an independent image, failing to track objects across consecutive frames. To address this issue, we propose a parametric Markov Chain Monte Carlo (MCMC) trend estimation algorithm that incorporates an Auto-Regressive (AR(p)) error assumption. We demonstrate that this MCMC algorithm achieves stationarity for the AR(p) model under specific constraints. Additionally, as a parametric method, the proposed algorithm can be applied to any time-related data, enabling the detection of underlying causes of trend changes for further analysis. Finally, we show that the proposed method can “stabilize” YOLOv7 detections, serving as an additional step to enhance the original algorithm’s performance. Full article
(This article belongs to the Special Issue Trends in Evolutionary Computation with Applications)
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