Interactive Genetic Algorithm Based on Collective Decision Making with Multi-user Collaboration
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摘要: 采用交互式遗传算法求解大数据信息检索问题时，为实现偏好信息的提取和优化，单用户需完成较多数量的人-机交互操作，由此易产生用户疲劳、算法搜索效率低的难题。对此，该文在算法中引入多用户并行策略，通过群体决策优势，提高样本利用效率。首先，根据优化目标性质确定共性化协同或个性化协同类型，基于用户浏览行为计算用户相似度和个体相似度。然后，通过共享偏好相似用户的偏好相似个体预测个体区间适应值。基于个体表现型相似度聚类，提出大规模种群个体“区间数-区间数”适应值赋值策略。最后，依据子代种群个体与父代种群最优个体的相似性，推荐用户最佳评价个体。将所提方法应用于装饰性墙壁纸选型问题，并与已有典型方法比较。结果表明，所提方法在推荐个体质量、减轻用户疲劳、提高搜索效率等方面均具有优越性。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.
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Key words:
- Genetic algorithms /
- Interaction /
- Collective decision making /
- Multi-user /
- Collaboration
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表 1 共性化协同结果
共性化协同次数 属性标准差 ${\omega _\gamma }$ 偏好相似个体数 ${N_m}$ 协同用户数k 协同耗时(s) 个体相似度 $\mu ({x_i},{x_B})$均值 ${x_{i1}}$ ${x_{i2}}$ ${x_{i3}}$ ${x_{i4}}$ ${x_{i5}}$ 1 1.43 0.65 0.54 0.69 1.73 24 4 53 0.63 2 1.38 0.41 0.43 0.37 1.52 35 9 67 0.78 3 1.24 0.22 0.24 0.32 1.38 48 13 109 0.86 
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表 2 用户相似度与个体相似度结果
算法 用户相似度 $M({u_a},{u_b})$均值 个体相似度 $\mu ({x_i},{x_B})$均值 第1次 第2次 第3次 第1次 第2次 第3次 本文算法 0.41 0.48 0.53 0.63 0.78 0.86 MiGA 0.36 0.42 0.47 0.54 0.66 0.73 GS-IGA 0.32 0.39 0.45 0.48 0.59 0.68
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表 3 算法优化解数目和成功率
算法 项目1 项目2 完全匹配解用户 不满意度DI均值 进化耗时(s) 相同解用户 不满意度DI均值 进化耗时(s) 本文算法 3 0.14 347 2 0.17 392 MiGA 2 0.21 438 0 0.26 492 GS-IGA 1 0.37 503 0 0.32 533
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表 4 优化结果形态分组
算法 优化结果属性分组/代表性个体 分组1 分组2 分组3 分组4 分组5 本文算法 (6, 190, 245, 34, 25) (6, 185, 220, 27, 25) (6, 202, 250, 27, 22) (6, 197, 230, 25, 17) $ \times $ MiGA (6, 241, 227, 38, 45) – (7, 145, 237, 36, 26) – (6, 188, 207, 29, 38) GS-IGA – (6, 190, 229, 37, 43) – $ \times $ $ \times $
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