Gray-Box Tunneling for Multi-Objective Combinatorial Optimization

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Multi-objective combinatorial optimization aims at simultaneously optimizing several, potentially conflicting, objective functions over a discrete decision space. In many combinatorial optimization problems, objective functions exhibit a gray-box structure , e.g., they can be decomposed into sub-functions involving only a restricted number of decision variables. Such structure can be exploited to design specialized and computationally efficient optimization algorithms. For example, for many binary optimization problems, objective functions can be represented using a bounded-degree Walsh/Fourrier transform , providing a natural framework for analyzing and exploiting variable interactions. Other combinatorial problem representations may also be considered, provided that they expose a suitable structure that can be leveraged by specialized gray-box evolutionary and search operators. While gray-box optimization has received increasing attention in the single-objective setting, the study of gray-box multi-objective optimization remains largely unexplored. This internship aims to contribute to this emerging research direction by developing new algorithmic approaches for efficiently navigating the landscape of multi-objective combinatorial optimization problems. A particular focus will be placed on Pareto Local Optima (PLOs) . PLOs generalize the notion of local optima to the multi-objecti