Please use this identifier to cite or link to this item: http://dspace.lib.uom.gr/handle/2159/28752
Author: Κωστάκη Κάσσανδρος, Βασίλειος
Kostakis Kassandros, Vasileios
Title: Combinatorial optimization using machine learning
Alternative Titles: Συνδυαστική βελτιστοποίηση με τη χρήση μηχανικής μάθησης
Date Issued: 2022
Department: Πρόγραμμα Μεταπτυχιακών Σπουδών Αναλυτική των Επιχειρήσεων και Επιστήμη των Δεδομένων
Supervisor: Καπάρης, Κωνσταντίνος
Abstract: Combinatorial optimization is a subfield of mathematical optimization that contains several hard problems with numerous real-life applications. The traditional way of solving combinatorial optimization problems relies on decisions taken based on expert knowledge and expert-designed heuristics. In recent years, a promising research line has brought an alternative way to light. This way is to automate decision- making for combinatorial optimization using machine learning. In this thesis, we provide general information on this research line but focus more on direct ways of leveraging machine learning to solve combinatorial optimization problems. Moreover, we create a reinforcement learning framework that learns a greedy constructive heuristic for the following graph combinatorial optimization problems: minimum vertex cover, maximum independent set and travelling salesman.
Keywords: Combinatorial optimization
Machine learning
Neural networks
Graph neural networks
Travelling salesman problem
Minimum vertex cover
Information: Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2022.
Appears in Collections:ΠΜΣ Αναλυτική των Επιχειρήσεων και Επιστήμη των Δεδομένων (Μ)

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