Reference : A RNN-Based Hyper-Heuristic for Combinatorial Problems

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/51074

Title :	A RNN-Based Hyper-Heuristic for Combinatorial Problems
Language :	English
Author, co-author :	Kieffer, Emmanuel [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
	Duflo, Gabriel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG >]
	Danoy, Grégoire [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
	Varrette, Sébastien [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Publication date :	2022
Main document title :	A RNN-Based Hyper-Heuristic for Combinatorial Problems
Peer reviewed :	Yes
Audience :	International
Event name :	Evolutionary Computation in Combinatorial Optimization: 22nd European Conference, EvoCOP 2022
Event date :	from 20-04-2022 to 22-04-2022
Keywords :	[en] Deep Symbolic Regression ; Multi-dimensional Knapsack ; Hyper-heuristics
Abstract :	[en] Designing efficient heuristics is a laborious and tedious task that generally requires a full understanding and knowledge of a given optimization problem. Hyper-heuristics have been mainly introduced to tackle this issue and are mostly relying on Genetic Programming and its variants. Many attempts in the literature have shown that an automatic training mechanism for heuristic learning is possible and can challenge human-based heuristics in terms of gap to optimality. In this work, we introduce a novel approach based on a recent work on Deep Symbolic Regression. We demonstrate that scoring functions can be trained using Recurrent Neural Networks to tackle a well-know combinatorial problem, i.e., the Multi-dimensional Knapsack. Experiments have been conducted on instances from the OR-Library and results show that the proposed modus operandi is an alternative and promising approach to human- based heuristics and classical heuristic generation approaches.
Permalink :	http://hdl.handle.net/10993/51074
DOI :	10.1007/978-3-031-04148-8_2

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