Abstract
In recent years, hyper-heuristics have received massive attention from the research community as an alternative of meta-heuristics. In a hyper-heuristic, generation or selection of an effective heuristic among a pool of heuristics is an important and challenging task in the search process. At each iteration, a suitable heuristic can take the search process toward the global optimal solution. Moreover, some additional factors such as quality and the number of heuristics also affect the performance. In this paper, we propose an evolutionary algorithm based hyper-heuristic framework that incorporates dynamic selection of parameters. To test its generality, effectiveness and robustness, we apply this approach on two different NP-hard problems - set packing problem (SPP) and minimum weight dominating set (MWDS) problem. The proposed approach for the SPP and the MWDS problem has been evaluated respectively on their respective set of benchmark instances. Computational results show that the proposed approach for the SPP and MWDS problem perform much better than their respective state-of-the-art approaches in terms of the solution quality and computational time.
| Original language | English |
|---|---|
| Pages (from-to) | 1-31 |
| Number of pages | 31 |
| Journal | Information Sciences |
| Volume | 505 |
| DOIs | |
| Publication status | Published - 2019 Dec 1 |
Fingerprint
Keywords
- Estimation of distribution algorithm
- Guided-mutation
- Heuristic
- Hyper-heuristic
- Minimum weight dominating set problem
- Set packing problem
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence
Cite this
An evolutionary algorithm based hyper-heuristic framework for the set packing problem. / Chaurasia, Sachchida Nand; Kim, Joong Hoon.
In: Information Sciences, Vol. 505, 01.12.2019, p. 1-31.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An evolutionary algorithm based hyper-heuristic framework for the set packing problem
AU - Chaurasia, Sachchida Nand
AU - Kim, Joong Hoon
PY - 2019/12/1
Y1 - 2019/12/1
N2 - In recent years, hyper-heuristics have received massive attention from the research community as an alternative of meta-heuristics. In a hyper-heuristic, generation or selection of an effective heuristic among a pool of heuristics is an important and challenging task in the search process. At each iteration, a suitable heuristic can take the search process toward the global optimal solution. Moreover, some additional factors such as quality and the number of heuristics also affect the performance. In this paper, we propose an evolutionary algorithm based hyper-heuristic framework that incorporates dynamic selection of parameters. To test its generality, effectiveness and robustness, we apply this approach on two different NP-hard problems - set packing problem (SPP) and minimum weight dominating set (MWDS) problem. The proposed approach for the SPP and the MWDS problem has been evaluated respectively on their respective set of benchmark instances. Computational results show that the proposed approach for the SPP and MWDS problem perform much better than their respective state-of-the-art approaches in terms of the solution quality and computational time.
AB - In recent years, hyper-heuristics have received massive attention from the research community as an alternative of meta-heuristics. In a hyper-heuristic, generation or selection of an effective heuristic among a pool of heuristics is an important and challenging task in the search process. At each iteration, a suitable heuristic can take the search process toward the global optimal solution. Moreover, some additional factors such as quality and the number of heuristics also affect the performance. In this paper, we propose an evolutionary algorithm based hyper-heuristic framework that incorporates dynamic selection of parameters. To test its generality, effectiveness and robustness, we apply this approach on two different NP-hard problems - set packing problem (SPP) and minimum weight dominating set (MWDS) problem. The proposed approach for the SPP and the MWDS problem has been evaluated respectively on their respective set of benchmark instances. Computational results show that the proposed approach for the SPP and MWDS problem perform much better than their respective state-of-the-art approaches in terms of the solution quality and computational time.
KW - Estimation of distribution algorithm
KW - Guided-mutation
KW - Heuristic
KW - Hyper-heuristic
KW - Minimum weight dominating set problem
KW - Set packing problem
UR - http://www.scopus.com/inward/record.url?scp=85069721292&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069721292&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2019.07.073
DO - 10.1016/j.ins.2019.07.073
M3 - Article
VL - 505
SP - 1
EP - 31
JO - Information Sciences
JF - Information Sciences
SN - 0020-0255
ER -