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Algorithms
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Evolution of Algorithms in the Era of Generative AI
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Evolution of Algorithms in the Era of Generative AI

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 831

Special Issue Editors

Prof. Dr. Domenico Ursino

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Guest Editor
Department of Information Engineering, Polytechnic University of Marche, 60121 Ancona, Italy
Interests: complex networks; social network analysis; data engineering; deep learning; machine learning; blockchains; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Gianluca Bonifazi

E-Mail Website
Guest Editor
Department of Information Engineering (DII), Polytechnic University of Marche, 60131 Ancona, Italy
Interests: social network analysis; machine learning; blockchains; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Enrico Corradini

E-Mail Website
Guest Editor
Department of Information Engineering (DII), Marche Polytechnic University, Ancona, Italy
Interests: data science; social and complex network analysis; graph mining; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Michele Marchetti

E-Mail
Guest Editor
Department of Information Engineering, Polytechnic University of Marche, 60121 Ancona, Italy
Interests: complex networks; social network analysis; deep learning; machine learning; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for a Special Issue of Algorithms entitled “Evolution of Algorithms in the Era of Generative AI”. This Special Issue is intended to be a venue for researchers, academics, and practitioners to share their latest findings and applications for new programming techniques for efficient and effective problem-solving in the Generative AI landscape and new algorithms to solve open problems taking into account the main features and strengths of Generative AI.

The Generative AI landscape has changed radically in recent years, both in terms of research and industry. Generative AI has transitioned from being a simple experiment to a strategic imperative for all those (academics and industrialists) who want to keep up with the times. Various organizations, both academic and industrial, are starting to use Generative AI not only in pilot programs, but increasingly in full-scale programs. The use of Generative AI promises enormous benefits in terms of improving efficiency, accelerating innovation, and creating new revenue streams. However, the rapid development of this new technology brings with it the need to address new challenges, define appropriate governance, mitigate risk, upskill talent, and manage regulatory uncertainty.

The primary goal of this Special Issue is to consolidate and disseminate knowledge on the theoretical aspects and applications of Generative AI algorithms and programming techniques, and to stimulate interdisciplinary collaboration and new discussions on the topic.

We encourage the submission of original, high-quality papers that address the challenges related to the evolution of programming techniques when used in the Generative AI landscape and the development of algorithms to address issues with the usage of Generative AI in different areas. Potential topics for this Special Issue include, but are not limited to, the following:

  • Design and evolution of programming paradigms tailored for Generative AI environments;
  • Programming frameworks and libraries to efficiently integrate Generative AI capabilities;
  • Hybrid algorithmic techniques combining traditional AI/ML with Generative AI;
  • Optimization algorithms leveraging generative models for combinatorial and numerical problems;
  • Meta-learning and self-improving algorithms in generative contexts;
  • Algorithmic foundations of prompt engineering and optimization;
  • Algorithms for model fine-tuning, adaptation, and personalization of generative models;
  • Robustness, reliability, and generalization techniques in Generative AI algorithms;
  • Evaluation metrics and benchmarking strategies for generative models and outputs;
  • Algorithms for bias mitigation, fairness, and explainability in Generative AI;
  • Scalable training and inference algorithms for large-scale generative models;
  • Generative AI algorithms in low-resource or edge-computing environments;
  • Federated and distributed learning approaches for generative models;
  • Algorithms for secure and privacy-preserving Generative AI;
  • Domain-specific generative modeling and algorithm design for:
    • Financial Services (e.g., fraud detection, algorithmic trading, synthetic data);
    • Retail and E-commerce (e.g., personalized recommendations, product design);
    • Manufacturing and Industry 4.0 (e.g., digital twins, predictive maintenance);
    • Healthcare and Biomedical Domains (e.g., drug discovery, synthetic patient data);
    • Education and EdTech (e.g., personalized learning systems, tutoring agents);
    • Data Management and Governance (e.g., synthetic data for privacy, data curation);
    • Marketing and Customer Engagement (e.g., campaign generation, sentiment modeling).
  • Responsible and ethical design of algorithms for Generative AI deployment;
  • Sustainable and energy-efficient algorithms for training and running generative models;
  • Regulatory and compliance-aware algorithm design for generative technologies;
  • Generative AI for algorithm design: using AI to invent or improve classical algorithms;
  • Cross-disciplinary applications of Generative AI in science, engineering, humanities, and social sciences;
  • Educational algorithms for teaching Generative AI concepts and programming techniques.

We are looking for your valuable contributions and are ready for a fruitful exchange of ideas and knowledge. Please do not hesitate to contact us for further information.

Prof. Dr. Domenico Ursino
Dr. Gianluca Bonifazi
Dr. Enrico Corradini
Dr. Michele Marchetti
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. 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 1800 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

  • generative AI
  • large language models
  • transformers
  • financial services
  • creative industries
  • retail
  • manufacturing
  • healthcare
  • education
  • data management
  • marketing
  • responsible generative AI
  • sustainable generative AI
  • ethics in generative AI

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

20 pages, 1088 KiB  
Article
The Specialist’s Paradox: Generalist AI May Better Organize Medical Knowledge
by Carlo Galli, Maria Teresa Colangelo, Marco Meleti and Elena Calciolari
Algorithms 2025, 18(7), 451; https://doi.org/10.3390/a18070451 - 21 Jul 2025
Abstract
This study investigates the ability of six pre-trained sentence transformers to organize medical knowledge by performing unsupervised clustering on 70 high-level Medical Subject Headings (MeSH) terms across seven medical specialties. We evaluated models from different pre-training paradigms: general-purpose, domain-adapted, and from-scratch domain-specific. The [...] Read more.
This study investigates the ability of six pre-trained sentence transformers to organize medical knowledge by performing unsupervised clustering on 70 high-level Medical Subject Headings (MeSH) terms across seven medical specialties. We evaluated models from different pre-training paradigms: general-purpose, domain-adapted, and from-scratch domain-specific. The results reveal a clear performance hierarchy. A top tier of models, including the general-purpose MPNet and the domain-adapted BioBERT and RoBERTa, produced highly coherent, specialty-aligned clusters (Adjusted Rand Index > 0.80). Conversely, models pre-trained from scratch on specialized corpora, such as PubMedBERT and BioClinicalBERT, performed poorly (Adjusted Rand Index < 0.51), with BioClinicalBERT yielding a disorganized clustering. These findings challenge the assumption that domain-specific pre-training guarantees superior performance for all semantic tasks. We conclude that model architecture, alignment between the pre-training objective and the downstream task, and the nature of the training data are more critical determinants of success for creating semantically coherent embedding spaces for medical concepts. Full article
(This article belongs to the Special Issue Evolution of Algorithms in the Era of Generative AI)
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21 pages, 561 KiB  
Article
Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
by Yahya Masri, Zifu Wang, Anusha Srirenganathan Malarvizhi, Samir Ahmed, Tayven Stover, David W. S. Wong, Yongyao Jiang, Yun Li, Qian Liu, Mathieu Bere, Daniel Rothbart, Dieter Pfoser and Chaowei Yang
Algorithms 2025, 18(7), 420; https://doi.org/10.3390/a18070420 - 8 Jul 2025
Viewed by 340
Abstract
To obtain actionable information for humanitarian and other emergency responses, an accurate classification of news or events is critical. Daily news and social media are hard to classify based on conveyed information, especially when multiple categories of information are embedded. This research used [...] Read more.
To obtain actionable information for humanitarian and other emergency responses, an accurate classification of news or events is critical. Daily news and social media are hard to classify based on conveyed information, especially when multiple categories of information are embedded. This research used large language models (LLMs) and traditional transformer-based models, such as BERT, to classify news and social media events using the example of the Sudan Conflict. A systematic evaluation framework was introduced to test GPT models using Zero-Shot prompting, Retrieval-Augmented Generation (RAG), and RAG with In-Context Learning (ICL) against standard and hyperparameter-tuned bert-based and bert-large models. BERT outperformed GPT in F1-score and accuracy for multi-label classification (MLC) while GPT outperformed BERT in accuracy for Single-Label classification from Multi-Label Ground Truth (SL-MLG). The results illustrate that a larger model size improves classification accuracy for both BERT and GPT, while BERT benefits from hyperparameter tuning and GPT benefits from its enhanced contextual comprehension capabilities. By addressing challenges such as overlapping semantic categories, task-specific adaptation, and a limited dataset, this study provides a deeper understanding of LLMs’ applicability in constrained, real-world scenarios, particularly in highlighting the potential for integrating NLP with other applications such as GIS in future conflict analyses. Full article
(This article belongs to the Special Issue Evolution of Algorithms in the Era of Generative AI)
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Graphical abstract

23 pages, 787 KiB  
Article
Integrating Machine Learning Techniques and the Theory of Planned Behavior to Assess the Drivers of and Barriers to the Use of Generative Artificial Intelligence: Evidence in Spain
by Antonio Pérez-Portabella, Jorge de Andrés-Sánchez, Mario Arias-Oliva and Mar Souto-Romero
Algorithms 2025, 18(7), 410; https://doi.org/10.3390/a18070410 - 3 Jul 2025
Viewed by 232
Abstract
Generative artificial intelligence (GAI) is emerging as a disruptive force, both economically and socially, with its use spanning from the provision of goods and services to everyday activities such as healthcare and household management. This study analyzes the enabling and inhibiting factors of [...] Read more.
Generative artificial intelligence (GAI) is emerging as a disruptive force, both economically and socially, with its use spanning from the provision of goods and services to everyday activities such as healthcare and household management. This study analyzes the enabling and inhibiting factors of GAI use in Spain based on a large-scale survey conducted by the Spanish Center for Sociological Research on the use and perception of artificial intelligence. The proposed model is based on the Theory of Planned Behavior and is fitted using machine learning techniques, specifically decision trees, Random Forest extensions, and extreme gradient boosting. While decision trees allow for detailed visualization of how variables interact to explain usage, Random Forest provides an excellent model fit (R2 close to 95%) and predictive performance. The use of Shapley Additive Explanations reveals that knowledge about artificial intelligence, followed by innovation orientation, is the main explanatory variable of GAI use. Among sociodemographic variables, Generation X and Z stood out as the most relevant. It is also noteworthy that the perceived privacy risk does not show a clear inhibitory influence on usage. Factors representing the positive consequences of GAI, such as performance expectancy and social utility, exert a stronger influence than the negative impact of hindering factors such as perceived privacy or social risks. Full article
(This article belongs to the Special Issue Evolution of Algorithms in the Era of Generative AI)
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