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Mathematics
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Machine Learning and Evolutionary Algorithms: Theory and Applications
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Machine Learning and Evolutionary Algorithms: Theory and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2502

Special Issue Editor

Dr. Saúl Zapotecas-Martínez

E-Mail Website
Guest Editor
Instituto Nacional de Astrofísica Óptica y Electrónica, Tonantzintla, Puebla 72840, Mexico
Interests: multi-objective optimization; decomposition-based optimization; bio-inspired computing; neuroevolution; neural architecture search; surrogate models for expensive optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The Special Issue on "Machine Learning and Evolutionary Algorithms: Theory and Applications" aims to highlight the latest advancements in the field of machine learning and its interaction with evolutionary algorithms.

Machine learning is a branch of artificial intelligence where computers learn to perform tasks without explicit programming. It involves algorithms that analyze data, identify patterns, and make predictions or decisions based on their experience. Machine learning algorithms involve training a model by using a dataset, tuning its parameters to minimize errors, and evaluating its performance. Machine learning algorithms have been successfully employed in different real-world applications. Their effectiveness relies on quality data, proper feature engineering, and avoiding overfitting.

Evolutionary computation is a line of research into artificial intelligence inspired by the principles of biological evolution. It encompasses a family of algorithms that mimic evolution to solve complex problems. The process begins with an initial population of potential solutions represented as individuals. Genetic operators like mutation and crossover generate new candidate solutions in successive generations. Over time, the population evolves, converging towards better solutions. Evolutionary algorithms are commonly employed in optimization, search, and decision-making problems where traditional mathematical programming methods are impractical or inefficient. Their versatility and ability to handle complex, multi-dimensional, and non-linear problems make them valuable in diverse real-world applications.

This Special Issue features a diverse collection of innovative studies that explore the increased synergy between machine learning and evolutionary algorithms for solving complex real-world problems. The research papers delve into various domains, including but not limited to:

  • Optimization of machine learning and evolutionary algorithms;
  • Complexity analysis of both machine learning and evolutionary algorithms;
  • Evolutionary optimization under uncertainty;
  • Evolutionary supervised/unsupervised/semi-supervised learning;
  • Hyper-parameter optimization;
  • New evolutionary operators;
  • Large-scale optimization;
  • Many-objective optimization;
  • Constrained single- and multi-objective optimization;
  • Data-driven optimization;
  • Surrogate-assisted evolutionary optimization;
  • Machine learning for data processing;
  • Hybrid algorithms and (meta)heuristics;
  • Feature selection and pattern recognition;
  • Computer vision;
  • Image processing;
  • Multi-task optimization;
  • Machine learning and evolutionary computation for solving real-world applications.

Overall, developing new evolutionary algorithms assisted by machine learning strategies, using evolutionary algorithms to optimize machine learning models, exploring the combination of evolutionary algorithms with deep learning or reinforcement learning methods, and expanding the possibilities for innovative applications in artificial intelligence will all be of interest to this Special Issue.

Dr. Saúl Zapotecas-Martínez
Guest Editor

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. Mathematics 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 2600 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

  • optimization of machine learning
  • evolutionary algorithms
  • new evolutionary operators
  • large-scale optimization
  • many-objective optimization
  • computer vision
  • image processing

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Published Papers (3 papers)

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Research

36 pages, 7184 KiB  
Article
Elite Evolutionary Discrete Particle Swarm Optimization for Recommendation Systems
by Shanxian Lin, Yifei Yang, Yuichi Nagata and Haichuan Yang
Mathematics 2025, 13(9), 1398; https://doi.org/10.3390/math13091398 - 24 Apr 2025
Viewed by 87
Abstract
Recommendation systems (RSs) play a vital role in e-commerce and content platforms, yet balancing efficiency and recommendation quality remains challenging. Traditional deep models are computationally expensive, while heuristic methods like particle swarm optimization struggle with discrete optimization. To address these limitations, this paper [...] Read more.
Recommendation systems (RSs) play a vital role in e-commerce and content platforms, yet balancing efficiency and recommendation quality remains challenging. Traditional deep models are computationally expensive, while heuristic methods like particle swarm optimization struggle with discrete optimization. To address these limitations, this paper proposes elite-evolution-based discrete particle swarm optimization (EEDPSO), a novel framework specifically designed to optimize high-dimensional combinatorial recommendation tasks. EEDPSO restructures the velocity and position update mechanisms to operate effectively in discrete spaces, integrating neighborhood search, elite evolution strategies, and roulette-wheel selection to balance exploration and exploitation. Experiments on the MovieLens and Amazon datasets show that EEDPSO outperforms five metaheuristic algorithms (GA, DE, SA, SCA, and PSO) in both recommendation quality and computational efficiency. For datasets below the million-level scale, EEDPSO also demonstrates superior performance compared to deep learning models like FairGo. The results establish EEDPSO as a robust optimization strategy for recommendation systems that effectively handles the cold-start problem. Full article
(This article belongs to the Special Issue Machine Learning and Evolutionary Algorithms: Theory and Applications)
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17 pages, 905 KiB  
Article
BERT Mutation: Deep Transformer Model for Masked Uniform Mutation in Genetic Programming
by Eliad Shem-Tov, Moshe Sipper and Achiya Elyasaf
Mathematics 2025, 13(5), 779; https://doi.org/10.3390/math13050779 - 26 Feb 2025
Viewed by 643
Abstract
We introduce BERT mutation, a novel, domain-independent mutation operator for Genetic Programming (GP) that leverages advanced Natural Language Processing (NLP) techniques to improve convergence, particularly using the Masked Language Modeling approach. By combining the capabilities of deep reinforcement learning and the BERT transformer [...] Read more.
We introduce BERT mutation, a novel, domain-independent mutation operator for Genetic Programming (GP) that leverages advanced Natural Language Processing (NLP) techniques to improve convergence, particularly using the Masked Language Modeling approach. By combining the capabilities of deep reinforcement learning and the BERT transformer architecture, BERT mutation intelligently suggests node replacements within GP trees to enhance their fitness. Unlike traditional stochastic mutation methods, BERT mutation adapts dynamically by using historical fitness data to optimize mutation decisions, resulting in more effective evolutionary improvements. Through comprehensive evaluations across three benchmark domains, we demonstrate that BERT mutation significantly outperforms conventional and state-of-the-art mutation operators in terms of convergence speed and solution quality. This work represents a pivotal step toward integrating state-of-the-art deep learning into evolutionary algorithms, pushing the boundaries of adaptive optimization in GP. Full article
(This article belongs to the Special Issue Machine Learning and Evolutionary Algorithms: Theory and Applications)
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30 pages, 10109 KiB  
Article
AI-Powered Approaches for Hypersurface Reconstruction in Multidimensional Spaces
by Kostadin Yotov, Emil Hadzhikolev, Stanka Hadzhikoleva and Mariyan Milev
Mathematics 2024, 12(20), 3285; https://doi.org/10.3390/math12203285 - 19 Oct 2024
Viewed by 1212
Abstract
The present article explores the possibilities of using artificial neural networks to solve problems related to reconstructing complex geometric surfaces in Euclidean and pseudo-Euclidean spaces, examining various approaches and techniques for training the networks. The main focus is on the possibility of training [...] Read more.
The present article explores the possibilities of using artificial neural networks to solve problems related to reconstructing complex geometric surfaces in Euclidean and pseudo-Euclidean spaces, examining various approaches and techniques for training the networks. The main focus is on the possibility of training a set of neural networks with information about the available surface points, which can then be used to predict and complete missing parts. A method is proposed for using separate neural networks that reconstruct surfaces in different spatial directions, employing various types of architectures, such as multilayer perceptrons, recursive networks, and feedforward networks. Experimental results show that artificial neural networks can successfully approximate both smooth surfaces and those containing singular points. The article presents the results with the smallest error, showcasing networks of different types, along with a technique for reconstructing geographic relief. A comparison is made between the results achieved by neural networks and those obtained using traditional surface approximation methods such as Bézier curves, k-nearest neighbors, principal component analysis, Markov random fields, conditional random fields, and convolutional neural networks. Full article
(This article belongs to the Special Issue Machine Learning and Evolutionary Algorithms: Theory and Applications)
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