Improving stochastic local search for uniform k ‐SAT by generating appropriate initial assignment-Reference-Cited by-同舟云学术

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Improving stochastic local search for uniform k ‐SAT by generating appropriate initial assignment

Published:2021-08-10 Issue:4 Volume:37 Page:1706-1744
ISSN:0824-7935
Container-title:Computational Intelligence
language:en
Short-container-title:Computational Intelligence

Author:

Fu Huimin¹^ORCID,Zhang Wuyang²,Wu Guanfeng³,Xu Yang³,Liu Jun⁴

Affiliation:

1. International Affairs Secretary School of Economic Information Engineering Southwest University of Finance and Economics Chengdu China

2. ChinaResources Beer (Holdings) Company Limited Chengdu China

3. National‐Local Joint Engineering Laboratory of System Credibility Automatic Verification Southwest Jiaotong University Chengdu China

4. School of Computing Ulster University Northern Ireland UK

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Wiley

Subject

Artificial Intelligence,Computational Mathematics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12438

Reference50 articles.

1. FuHM XuY WuGF NingXR.An improved genetic algorithm for solving 3‐SAT problems based on effective restart and greedy strategy. Paper presented at: Proceedings of the ISKE‐17 Nanjing China; 2017:1‐6.

2. LiangJH.OhC GaneshV CzarneckiH PoupartP.MapleCOMSPS LRB VSIDS and MapleCOMSPS CHB VSIDS. Proceeding of the SAT‐2017 Melbourne Australia; 2017:20‐21.

3. LuoM LiCM XiaoF ManyàF LüZ.An effective learnt clause minimization approach for CDCL SAT solvers. Paper presented at: Proceedings of the AAAI‐17 San Francisco California USA; 2017:703‐711.

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