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Trustworthy and Sustainable Automate Design of Algorithms for the Generative AI Era

Project description

Explainable, transparent and sustainable automated algorithm design

The complexity of computational problems across scientific and industrial domains has increased the need for automated approaches to algorithm design. In the case of large language models, a new paradigm of automated design of algorithms (ADA) has emerged. It enables direct evolution of algorithm implementations from a broader set of candidate operators and removes the need for handcrafted structures. However, current ADA research focuses mainly on performance, often overlooking trustworthiness, explainability and sustainability, which are just as important. Supported by the Marie Skłodowska-Curie Actions programme, the TSADA project is developing explainable and efficient ADA methods by improving transparency and resource use. It proposes sustainable techniques that reuse prior knowledge to reduce redundant training.

Objective

Algorithms are the key to solving complex problems across diverse research and application domains. However, traditional algorithm design relies heavily on human expertise, making algorithm design a costly and time-consuming process. While recent advances in Algorithm Configuration (AC) have partially automated the design of algorithms, their success still strongly relies on human-crafted algorithmic frameworks, limiting broader adoption across domains. With the advent of Large Language Models (LLMs), a new paradigm for Automated Design of Algorithms (ADA) has emerged, enabling the direct evolution of algorithm implementation from a significantly broader set of candidate operators, eliminating the need for handcrafted frameworks. Despite these advances, current ADA research primarily focuses on achieving competitive performance while neglecting trustworthiness, explainability, and sustainability, which are critical for practical deployment.
This project, TSADA, aims to address these issues by developing methods to enhance the explainability, transparency, and efficiency of ADA, including both traditional AC and LLM-driven approaches. Through working on the design of Satisfiability (SAT) solvers, I will propose sustainable ADA techniques capable of leveraging knowledge of prior algorithms to minimize redundant training tasks. Furthermore, I will systematically compare traditional AC and LLM-driven ADA methodologies to provide evidence-based guidelines for selecting the most suitable approach under different scenarios. By advancing explainable and resource-efficient ADA, TSADA contributes to the broader objectives of Explainable AI and Green AI, paving the way for more trustworthy, transparent, and sustainable algorithmic innovation.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

SORBONNE UNIVERSITE
Net EU contribution
€ 226 420,56
Address
21 RUE DE L'ECOLE DE MEDECINE
75006 Paris
France
Region
Ile-de-France Ile-de-France Paris
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data