Paper published in a book (Scientific congresses, symposiums and conference proceedings)
A RNN-Based Hyper-Heuristic for Combinatorial Problems
KIEFFER, Emmanuel; DUFLO, Gabriel; DANOY, Grégoire et al.
2022In A RNN-Based Hyper-Heuristic for Combinatorial Problems
Peer reviewed
 

Files


Full Text
A_RNN_based_Hyper_heuristic_for_combinatorial_problems.pdf
Author preprint (563.21 kB)
Download

All documents in ORBilu are protected by a user license.

Send to



Details



Keywords :
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.
Disciplines :
Computer science
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)
External co-authors :
no
Language :
English
Title :
A RNN-Based Hyper-Heuristic for Combinatorial Problems
Publication date :
2022
Event name :
Evolutionary Computation in Combinatorial Optimization: 22nd European Conference, EvoCOP 2022
Event date :
from 20-04-2022 to 22-04-2022
Audience :
International
Main work title :
A RNN-Based Hyper-Heuristic for Combinatorial Problems
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Available on ORBilu :
since 22 May 2022

Statistics


Number of views
216 (37 by Unilu)
Number of downloads
179 (5 by Unilu)

Scopus citations®
 
0
Scopus citations®
without self-citations
0
WoS citations
 
0

Bibliography


Similar publications



Contact ORBilu