Interactive Genetic Algorithm Based on Collective Decision Making with Multi-user Collaboration

Guangsong GUO; Zhenhua WEN; Guosheng HAO

doi:10.11999/JEIT171234

Volume 40 Issue 9

Aug. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(9): 2165-2172

Guangsong GUO, Zhenhua WEN, Guosheng HAO. Interactive Genetic Algorithm Based on Collective Decision Making with Multi-user Collaboration[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2165-2172. doi: 10.11999/JEIT171234

Citation:

Guangsong GUO, Zhenhua WEN, Guosheng HAO. Interactive Genetic Algorithm Based on Collective Decision Making with Multi-user Collaboration[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2165-2172. doi: 10.11999/JEIT171234

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Interactive Genetic Algorithm Based on Collective Decision Making with Multi-user Collaboration

doi: 10.11999/JEIT171234

1.
School of Mechatronics Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
2.
College of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, China

Funds: The National Natural Science Foundation of China (61673196), The Science and Technology Research Project of Henan Province (172102210513), The Key Scientific Research Project in Colleges and Universities of Henan Province (18A120012)

Received Date: 2017-12-28
Rev Recd Date: 2018-05-16

Available Online: 2018-07-12

Publish Date: 2018-09-01

Abstract

Abstract

When using interactive genetic algorithm to solve big data information retrieval problem, single user needs to complete more human-machine interactive operation to achieve preference information extraction and optimization, thus it is easy to generate the problem of user fatigue and algorithm low efficiency. A multi-user strategy is introduced by making full use of the advantages of group decision to improve the sample utilization efficiency. First of all, multi-user collaborative type is devided into common collaboration or personalized collaboration according to the optimization goal which calculats user similarity and individual similarity based on user’s browsing behaviors. Then, individuals’ interval fitness is forecasted by sharing similar individual of similarity users. Based on phenotype similarity clustering, the large scale population individuals of " interval-interval” fitness assignment strategy is introduced. Finally, the best evaluation individual is recommended according to the similarities between offspring individuals and parent individuals. The proposed method is applied to decorative wallpaper design problem and is compared with existing typical methods. The experimental results confirm that the proposed algorithm has advantages in improving optimization quality and alleviating user fatigue while improving its efficiency in exploration.
- Genetic algorithms,
- Interaction,
- Collective decision making,
- Multi-user,
- Collaboration

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References(18)

References

李清霞, 魏文红, 蔡昭权. 混合用户和项目协同过滤的电子商务个性化推荐算法[J]. 中山大学学报(自然科学版), 2016, 55(5): 37–42 doi: 10.13471/j.cnki.acta.snus.2016.05.007

LI Qingxia, WEI Wenhong, and CAI Zhaoquan. Hybrid user and item based collaborative filtering personalized recommendation algorithm in E-commerce[J]. ACTA Scientiarum Naturalium Universitatis Sunyatseni, 2016, 55(5): 37–42 doi: 10.13471/j.cnki.acta.snus.2016.05.007

王占, 林岩. 基于信任与用户兴趣变化的协同过滤方法研究[J]. 情报学报, 2017, 36(2): 197–205 doi: 10.3772/j.issn.1000-0135.2017.02.010

WANG Zhan and LIN Yan. Research on collaborative filtering method based on trust and the change of user’s interest[J]. Journal of the China Society for Scientific and Technical Information, 2017, 36(2): 197–205 doi: 10.3772/j.issn.1000-0135.2017.02.010

潘涛涛, 朱珂, 吴毅涛. 基于满意区间的协同过滤推荐算法[J]. 计算机应用研究, 2017, 34(8): 2282–2286 doi: 10.3969/j.issn.1001-3695.2017.08.009

PAN Taotao, ZHU Ke, and WU Yitao. Satisfactory intervals similarity-based collaborative filtering recommendation algorithm[J]. Application Research of Computers, 2017, 34(8): 2282–2286 doi: 10.3969/j.issn.1001-3695.2017.08.009

ALJAWAWDEH H J, SIMONS C L, and ODEH M. Metaheuristic design pattern: preference[C]. Proceedings of the Companion Publication of the Annual Conference on Genetic and Evolutionary Computation, Madrid, 2015: 1257–1260.

郭一楠, 巩敦卫, 周勇. 基于多智能体系统的协同交互式进化计算模型[J]. 系统仿真学报, 2005, 17(7): 1548–1552 doi: 10.3969/j.issn.1004-731X.2005.07.005

GUO Yinan, GONG Dunwei, and ZHOU Yong. Cooperative interactive evolutionary computation model based on multi-agent system[J]. Journal of System Simulation, 2005, 17(7): 1548–1552 doi: 10.3969/j.issn.1004-731X.2005.07.005

QUIROZ C, LOUIS J, and BANERJEE A. Towards creative design using collaborative interactive genetic algorithm[C]. Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, Trondheim, 2009: 1849–1856.

SAYAMA H and DIONNE S D. Studying collective human decision making and creativity with evolutionary computation[J]. Artificial Life, 2015, 21(3): 379–393 doi: 10.1162/ARTL_a_00178

SUN Xiaoyan, YANG Lei, and GONG Dunwei. Interactive genetic algorithm assisted with collective intelligence from group decision making[C]. IEEE Congress on Evolutionary Computation, Brisbane, 2012: 1–8.

GONG Dunwei, YANG Lei, and SUN Xiaoyan. Applying knowledge of users with similar preference to construct surrogate models of IGA[J]. Chinese Journal of Electronics, 2015, 24(3): 555–563 doi: 10.1049/cje.2015.07.020

SEYAMA T and MUNETOMO M. Development of a multi-player interactive genetic algorithm-based 3D modeling system for glasses[C]. IEEE Congress on Evolutionary Computation, Vancouver, 2016: 846–852.

GONG Dunwei, SUN Jing, and MIAO Zhuang. A set-based genetic algorithm for interval many-objective optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2018, 22(1): 47–60 doi: 10.1109/TEVC.2016.2634625

巩敦卫, 陈健. 基于精英集选择进化个体的交互式遗传算法[J]. 电子学报, 2014, 42(8): 1538–1544 doi: 10.3969/j.issn.0372-2112.2014.08.012

GONG Dunwei and CHEN Jian. Interactive genetic algorithms with selecting individuals using elite set[J]. Acta Electronica Sinica, 2014, 42(8): 1538–1544 doi: 10.3969/j.issn.0372-2112.2014.08.012

XUAN Jifeng, HE Jiang, REN Zhilei, et al. Solving the large scale next release problem with a backbone based multilevel algorithm[J]. IEEE Transactions on Software Engineering, 2012, 38(5): 1195–1212 doi: 10.1109/TSE.2011.92

巩敦卫, 陈健, 孙晓燕. 新的基于相似度估计个体适应值的交互式遗传算法[J]. 控制理论与应用, 2013, 30(5): 558–566 doi: 10.7641/CTA.2013.21164

GONG Dunwei, CHEN Jian, and SUN Xiaoyan. Novel interactive genetic algorithm for estimating individual fitness based on similarity[J]. Control Theory&Applications, 2013, 30(5): 558–566 doi: 10.7641/CTA.2013.21164

ALLYSSON A A, MATHEUS P, and ITALO Y. An architecture based on interactive optimization and machine learning applied to the next release problem[J]. Automated Software Engineering, 2017, 24(3): 623–649 doi: 10.1007/s10515-016-0200-3

KUZMAL M and ANDREJKOV G. Predicting user’s preferences using neural networks and psychology models[J]. Applied Intelligence, 2016, 44(3): 526–538 doi: 10.1007/s10489-015-0717-3

毛宜钰, 刘建勋, 胡蓉, 等. 基于Logistic函数和用户聚类的协同过滤算法[J]. 浙江大学学报(工学版), 2017, 51(6): 1252–1258 doi: 10.3785/j.issn.1008-973X.2017.06.024

MAO Yiyu, LIU Jianxun, HU Rong, et al. Collaborative filtering algorithm based on logistic function and user clustering[J]. Journal of Zhejiang University(Engineering Science.), 2017, 51(6): 1252–1258 doi: 10.3785/j.issn.1008-973X.2017.06.024

赵文涛, 成亚飞, 王春春. 基于Logistic 时间函数和用户特征的协同过滤算法[J]. 计算机应用与软件, 2017, 34(2): 285–289 doi: 10.3969/j.issn.1000-386x.2017.02.051

ZHAO Wentao, CHENG Yafei, and WANG Chunchun. Collaborative filtering algorithm based on Logistic time function and user features[J]. Computer Applications and Software, 2017, 34(2): 285–289 doi: 10.3969/j.issn.1000-386x.2017.02.051

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