Evolution of Algorithms in the Era of Generative AI

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 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.

• 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