Due to an acute illness of Dr. Bossek, the upper seminar scheduled for tomorrow can unfortunately not take place.
We regret this cancellation at short notice and will try to find a new date as soon as possible.
Computer Science Graduate Seminar
Wednesday, May 24, 2023, 1:00pm
Tailored Evolutionary Operators for the Multi-Objective Spanning Tree Problem
- Dr. Jakob Bossek - Computer Science Chair 14
- Place: Room 9222, Building E3, Ahornstr. 55
With this talk I want to introduce myself to the RWTH computer science department.
In the first part I will go into detail on paper recently accepted in the evolutionary computation journal (Bossek & Grimme, 2023) entitled “Tailored Evolutionary Operators for the Multi-Objective Spanning Tree Problem”. The second part will give a broader overview of my research foci and projects in the fields of Evolutionary Optimisation and Automated Artificial Intelligence.
The Minimum Spanning Tree (MST) problem is the challenge of finding a tree in an edge-weighted graph that maintains connectivity of all nodes and has minimal costs among all such trees. The MST problem is a fundamental combinatorial optimisation problem with countless applications, e.g., in the construction of communication networks, medical imaging, or many other areas that range from logistic via graph drawing to power grid network design. The basic single-objective version of the MST problem can be solved efficiently, i.e., in polynomial time, e.g., by Prim’s algorithm. The multi-objective MST (moMST) version though (i.e., multiple weights per edge) is NP-hard and suffers from intractability. Thus, efficient heuristics are needed to approximate the set of optimal trade-off solutions.
Evolutionary Algorithms are randomised search heuristics that are among the most successful when it comes to solving NP-hard multi-objective optimisation problems.
I will present recent work on the design of several highly biased sub-graph-based mutation operators for the moMST problem. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally (Pareto-)optimal sub-trees. The latter (biased) step is realised by applying Kruskal’s single-objective MST algorithm to a weighted sum scalarisation of a sub-graph.
I will detail some runtime complexity results for the introduced operators and demonstrate results that show that the sub-graph based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs.
The computer science lecturers invite interested people to join.