Reference : A RNN-Based Hyper-Heuristic for Combinatorial Problems |
Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
Engineering, computing & technology : Computer science | |||
Computational Sciences | |||
http://hdl.handle.net/10993/51074 | |||
A RNN-Based Hyper-Heuristic for Combinatorial Problems | |
English | |
Kieffer, Emmanuel ![]() | |
Duflo, Gabriel ![]() | |
Danoy, Grégoire ![]() | |
Varrette, Sébastien ![]() | |
Bouvry, Pascal ![]() | |
2022 | |
A RNN-Based Hyper-Heuristic for Combinatorial Problems | |
Yes | |
International | |
Evolutionary Computation in Combinatorial Optimization: 22nd European Conference, EvoCOP 2022 | |
from 20-04-2022 to 22-04-2022 | |
[en] Deep Symbolic Regression ; Multi-dimensional Knapsack ; Hyper-heuristics | |
[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. | |
http://hdl.handle.net/10993/51074 | |
10.1007/978-3-031-04148-8_2 |
File(s) associated to this reference | ||||||||||||||
Fulltext file(s):
| ||||||||||||||
All documents in ORBilu are protected by a user license.