Symmetry/Asymmetry in Evolutionary Algorithms

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1626

Special Issue Editors


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Guest Editor
Escuela Internacional de Ingeniería, Universidad Anáhuac, Cancún 77565, Quintana Roo, Mexico
Interests: evolutionary algorithms; machine learning; artificial intelligence

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Guest Editor
Computer Science and Information Systems (CSIS) Department, University of Limerick, V94 T9PX Limerick, Ireland
Interests: health technologies; evolutionary algorithms; machine learning

Special Issue Information

Dear Colleagues,

We are delighted to announce the launch of a Special Issue on “Symmetry/Asymmetry in Evolutionary Algorithms". In the last decade, computing power has increased as well as the demand for solving highly complex optimization problems where approximate optimization with bio-inspired techniques has been demonstrated as an excellent choice in decision-making processes. This Special Issue aims to explore the current impact and advances of evolutionary algorithms in addressing and solving complex real-world problems. We invite researchers from the fields of theoretical analysis of evolutionary algorithms, artificial intelligence, machine learning, soft computing, bio-inspired computing, evolutionary deep learning, and hybrid evolutionary approaches to contribute their innovative works.

We encourage researchers to submit their original research articles, reviews, or perspectives that explore the role of symmetry and asymmetry in evolutionary algorithms. This Special Issue provides an opportunity to share novel insights, methodologies, and applications, shaping the future of the field.

Dr. Enrique Naredo
Prof. Dr. Conor Ryan
Guest Editors

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Keywords

  • genetic algorithms
  • genetic programming
  • grammatical evolution
  • evolutionary computation
  • evolutionary programming
  • evolution strategies
  • differential evolution
  • swarm intelligence
  • particle swarm optimization
  • artificial bee colony
  • ant colony optimization
  • artificial immune systems
  • memetic algorithms
  • large-scale optimization problems
  • cuckoo search algorithms
  • symbiotic organisms search
  • whale optimization algorithms
  • butterfly optimization algorithms
  • medical image processing and analysis
  • fundamentals of evolutionary algorithms
  • biometric recognition
  • optimization and learning methods
  • document analysis and recognition
  • video analysis and understanding
  • character recognition
  • visual applications and systems
  • face recognition and pose estimation
  • vision problems in robotics and autonomous driving
  • pattern classification and clustering analysis
  • object detection, tracking, and recognition
  • machine learning
  • action recognition
  • neural networks and deep learning
  • multimedia analysis and reasoning
  • feature extraction and feature selection
  • GPU implementation of evolutionary computing
  • symmetry

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Published Papers (1 paper)

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29 pages, 1679 KiB  
Article
Symmetrical Generalized Pareto Dominance and Adjusted Reference Vector Cooperative Evolutionary Algorithm for Many-Objective Optimization
by Shuwei Zhu, Liusheng Zeng and Meiji Cui
Symmetry 2024, 16(11), 1484; https://doi.org/10.3390/sym16111484 - 6 Nov 2024
Viewed by 1093
Abstract
In Pareto-based many-objective evolutionary algorithms, performance usually degrades drastically as the number of objectives increases due to the poor discriminability of Pareto optimality. Although some relaxed Pareto domination relations have been proposed to relieve the loss of selection pressure, it is hard to [...] Read more.
In Pareto-based many-objective evolutionary algorithms, performance usually degrades drastically as the number of objectives increases due to the poor discriminability of Pareto optimality. Although some relaxed Pareto domination relations have been proposed to relieve the loss of selection pressure, it is hard to maintain good population diversity, especially in the late phase of evolution. To solve this problem, we propose a symmetrical Generalized Pareto Dominance and Adjusted Reference Vectors Cooperative (GPDARVC) evolutionary algorithm to deal with many-objective optimization problems. The symmetric version of generalized Pareto dominance (GPD), as an efficient framework, provides sufficient selection pressure without degrading diversity, no matter of the number of objectives. Then, reference vectors (RVs), initially generated evenly in the objective space, guide the selection with good diversity. The cooperation of GPD and RVs in environmental selection in part ensures a good balance of convergence and diversity. Also, to further enhance the effectiveness of RV-guided selection, we regenerate more RVs according to the proportion of valid RVs; thereafter, we select the most valid RVs for adjustment after the association operation. To validate the performance of GPDARVC, we compare it with seven representative algorithms on commonly used sets of problems. This comprehensive analysis results in 26 test problems with different objective numbers and 6 practical problems, which show that GPDARVC outperforms other algorithms in most cases, indicating its great potential to solve many-objective optimization problems. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Evolutionary Algorithms)
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