Sehr geehrte Damen und Herren, aufgrund einer akuten Erkrankung von Herrn Dr. Bossek kann das für morgen angesetzte Oberseminar leider nicht stattfinden. Wir bedauern diese kurzfristige Absage und werden versuchen, zeitnah einen neuen Termin zu finden. Mit freundlichen Grüßen Im Auftrag Andrea Gibbels -- Andrea Gibbels, M.A. Administrative Assistant Chair for AI Methodology / Lehrstuhl für Methodik der Künstlichen Intelligenz RWTH Aachen University E-mail: secret@aim.rwth-aachen.de Phone: +49 241 80 21452 Theaterstr. 35-39 52062 Aachen Germany +********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 24. Mai 2023, 13.00 Uhr Ort: Raum 9222, Gebäude E3, Ahornstr. 55 Referent: Dr. Jakob Bossek Lehrstuhl Informatik 14 Thema: Tailored Evolutionary Operators for the Multi-Objective Spanning Tree Problem Abstract: 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. Es laden ein: die Dozentinnen und Dozenten der Informatik