Seminar Details
2025-10-30 (13:00) : Hybrid artificial intelligence for solving computationally hard problems
At Shannon, Maxwell a.105
Organized by Computer Science and Engineering
Speaker :
Quentin Cappart (ICTEAM)
Abstract :
Combinatorial optimization provides methods to make the best possible decisions in complex scenarios, with practical applications in areas such as transportation, logistics, and healthcare. Traditionally, solving methods for combinatorial problems (such as integer programming, constraint programming, or local search) have focused on solving isolated problem instances, often overlooking the fact that these instances frequently originate from related data distributions. In recent years, there has been a growing interest in leveraging machine learning, particularly neural networks, to enhance combinatorial solvers by utilizing historical data. Despite this interest, it remains unclear how to effectively integrate learning into such engines to boost overall performance. In this presentation, I will share my journey in tackling this challenge, from my initial attempts to my current research directions. I will offer personal advice for researchers interested in exploring this fascinating field, highlighting the potential and opportunities for designing efficient hybrid artificial intelligence for solving computationally hard problems.