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Algorithms
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Feature Papers in Evolutionary Algorithms and Machine Learning
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Feature Papers in Evolutionary Algorithms and Machine Learning

A topical collection in Algorithms (ISSN 1999-4893). This collection belongs to the section "Evolutionary Algorithms and Machine Learning".

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Editor

Prof. Dr. Stefano Mariani

E-Mail Website
Collection Editor
Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
Interests: MEMS; smart materials; micromechanics; machine learning-driven materials modeling
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

This Topical Collection focuses on the intersection of evolutionary algorithms and machine learning, bringing together recent advancements in optimization, automated learning, and intelligent systems. Evolutionary algorithms, inspired by natural selection, have proven to be powerful methods for tackling complex optimization and learning problems. Their integration with machine learning techniques has led to innovative solutions in feature selection, neural architecture search, reinforcement learning, and many other domains.

We invite high-quality contributions that explore novel methodologies, theoretical insights, and real-world applications at the interface of evolutionary algorithms and machine learning. Topics of interest include, but are not limited to, the methodological development and practical application of the following:

  • Evolutionary Algorithms for Machine Learning
    • Genetic algorithms, genetic programming, evolutionary strategies, and swarm intelligence.
    • Neuroevolution and neural architecture search (NAS).
  • Machine Learning for Evolutionary Algorithms
    • Surrogate-assisted evolutionary optimization.
    • Transfer learning and evolutionary strategies.
  • Hybrid and Advanced Techniques
    • Memetic algorithms and hybrid evolutionary approaches.
    • Bio-inspired computation and learning paradigms.

Prof. Dr. Stefano Mariani
Collection 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 collection 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. Algorithms 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 1600 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 algorithms
  • artificial intelligence
  • machine and deep learning

Published Papers (2 papers)

Download All Papers

2025

15 pages, 28684 KiB  
Article
Efficient Expiration Date Recognition in Food Packages for Mobile Applications
by Hao Peng, Juan Bayon, Joaquin Recas and Maria Guijarro
Algorithms 2025, 18(5), 286; https://doi.org/10.3390/a18050286 - 15 May 2025
Viewed by 70
Abstract
The manuscript introduces an innovative framework for expiration date recognition aimed at improving accessibility for visually impaired individuals. The study underscores the pivotal role of convolutional neural networks (CNNs) in addressing complex challenges, such as variations in typography and image degradation. The system [...] Read more.
The manuscript introduces an innovative framework for expiration date recognition aimed at improving accessibility for visually impaired individuals. The study underscores the pivotal role of convolutional neural networks (CNNs) in addressing complex challenges, such as variations in typography and image degradation. The system attained an F1-score of 0.9303 for the detection task and an accuracy of 97.06% for the recognition model, with a total inference time of 63 milliseconds on a single GeForce GTX 1080 GPU. A comparative analysis of quantized models—FP32, FP16, and INT8—emphasizes the trade-offs in inference speed, energy efficiency, and accuracy on mobile devices. The experimental results indicate that the FP16 model operating in CPU mode achieves an optimal equilibrium between precision and energy consumption, underscoring its suitability for resource-constrained environments. Full article
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19 pages, 755 KiB  
Review
Artificial Intelligence and the Human–Computer Interaction in Occupational Therapy: A Scoping Review
by Ioannis Kansizoglou, Christos Kokkotis, Theodoros Stampoulis, Erasmia Giannakou, Panagiotis Siaperas, Stavros Kallidis, Maria Koutra, Paraskevi Malliou, Maria Michalopoulou and Antonios Gasteratos
Algorithms 2025, 18(5), 276; https://doi.org/10.3390/a18050276 - 8 May 2025
Viewed by 290
Abstract
Occupational therapy (OT) is a client-centered health profession focused on enhancing individuals’ ability to perform meaningful activities and daily tasks, particularly for those recovering from injury, illness, or disability. As a core component of rehabilitation, it promotes independence, well-being, and quality of life [...] Read more.
Occupational therapy (OT) is a client-centered health profession focused on enhancing individuals’ ability to perform meaningful activities and daily tasks, particularly for those recovering from injury, illness, or disability. As a core component of rehabilitation, it promotes independence, well-being, and quality of life through personalized, goal-oriented interventions. Identifying and measuring the role of artificial intelligence (AI) in the human–computer interaction (HCI) within OT is critical for improving therapeutic outcomes and patient engagement. Despite AI’s growing significance, the integration of AI-driven HCI in OT remains relatively underexplored in the existing literature. This scoping review identifies and maps current research on the topic, highlighting applications and proposing directions for future work. A structured literature search was conducted using the Scopus and PubMed databases. Articles were included if their primary focus was on the intersection of AI, HCI, and OT. Out of 55 retrieved articles, 26 met the inclusion criteria. This work highlights three key findings: (i) machine learning, robotics, and virtual reality are emerging as prominent AI-driven HCI techniques in OT; (ii) the integration of AI-enhanced HCI offers significant opportunities for developing personalized therapeutic interventions; (iii) further research is essential to evaluate the long-term efficacy, ethical implications, and patient outcomes associated with AI-driven HCI in OT. These insights aim to guide future research efforts and clinical applications within this evolving interdisciplinary field. In conclusion, AI-driven HCI holds considerable promise for advancing OT practice, yet further research is needed to fully realize its clinical potential. Full article
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