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交互式进化计算中保持用户理性的最大进化代数

, PP. 781-785

Keywords: 进化计算,最大进化代数,用户理性,用户疲劳

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Abstract:

交互式进化计算中用户保持理性是算法全局收敛的重要条件,为确保用户保持理性,必须设计合理的最大进化代数。文中首先提出3类最大进化代数,其次,结合6种常见的适应度赋值方法分别研究最大进化代数的定量计算方法。理论分析和实验都表明,采用最值赋值和分等级赋值方法不仅切实可行,而且可以让用户在较大的代数内保持理性状态。文中研究为选择合适的适应度赋值方法提供参考依据。

References

[1]  Holland J H. Adaptation in Natural and Artificial Systems. Cambridge, USA: MIT Press, 1992
[2]  Gong Dunwei, Yuan Jie, Ma Xiaoping. Interactive Genetic Algorithms with Large Population Size // Proc of the IEEE Congress on Evolutionary Computation. Hongkong, China, 2008: 1678-1685
[3]  Hao Guosheng, Huang Yongqing, Zhang Yong, et al. Rational User-A Sufficient Condition for Global Convergence in Interactive Evolutionary Computation. Pattern Recognition and Artificial Intelligence, 2008, 21(4): 441-445 (in Chinese)(郝国生,黄永青,张 勇,等.理性用户——交互式进化计算全局收敛的一个充分条件.模式识别与人工智能, 2008, 21(4): 441-445)
[4]  Gong Dunwei, Yuan Jie. Impact of Individuals Fitness Expressions on Interactive Genetic Algorithms Performances // Proc of the Chinese Control and Decision Conference. Guilin, China, 2009: 2415-2420
[5]  Gong Dunwei, Sun Xiaoyan, Yuan Jie. Interactive Genetic Algorithm with Individuals Uncertain Fitness // dos Santos W P, ed. Evolutionary Computation. Vienna, Austria: In-Tech Publisher, 2009
[6]  Hu Jing. Interactive Genetic Algorithm with Image Information Retrieval. Master Dissertation. Hefei, China: University of Science and Technology of China. School of Computer Science and Technology, 2001 (in Chinese)(胡 静.用于图形图象信息检索的交互式遗传算法.硕士学位论文.合肥:中国科技大学.计算机科学与技术系, 2001)
[7]  Zhou Yong, Gong Dunwei, Hao Guosheng, et al. Neural Network Based Phase Estimation of Individual Fitness in Interactive Genetic Algorithm. Control and Decision, 2005, 20(2): 234-236,240 (in Chinese)(周 勇,巩敦卫,郝国生,等.交互式遗传算法基于NN的个体适应度分阶段估计.控制与决策, 2005, 20(2): 234-236,240)
[8]  Gong Dunwei, Hao Guosheng, Zhou Yong, et al. Interactive Genetic Algorithms with Multi-Population Adaptive Hierarchy and Their Application in Fashion Design. Applied Mathematics and Computation, 2007, 185(2): 1098-1108
[9]  Takagi H, Kishi K. On-line Knowledge Embedding for an Interactive EC-Based Montage System // Proc of the International Conference on Knowledge-Based Intelligent Information Engineering Systems. Adelaide, Australia, 1999: 280-283
[10]  Denis P, Philippe C, Thierry B, et al. Eye-Tracking Evolutionary Algorithm to Minimize User Fatigue in IEC Applied to Interactive One-Max Problem // Proc of the Genetic Evolutionary Computation Conference. London, UK, 2007: 2883-2886
[11]  Gong Dunwei, Guo Guangsong, Lu Li, et al. Adaptive Interactive Genetic Algorithms with Interval Fitness of Evolutionary Individuals. Progress in Natural Science, 2008, 18(3): 359-365
[12]  Sugimoto F, Yoneyama M. Robustness against Instability of Sensory Judgment in a Human Interface to Draw a Facial Image Using a Psychometrical Space Model // Proc of the IEEE International Conference on Multimedia and Expo. New York, USA, 2000, Ⅱ: 635-638
[13]  Sugimoto F, Yoneyama M. Hybrid Fitness Assignment Strategy in IGA-A Method to Compose Fitness // Proc of the IEEE Workshop on Multimedia Signal Processing. Vingin Islands, USA, 2002: 284- 287
[14]  Huang Yongqing, Lu Qing, Liang Changyong, et al. Interactive Multi-Agent Evolutionary Algorithm and Its Application. Journal of System Simulation, 2006, 18(7): 2030-2032,2055 (in Chinese)(黄永青, 陆 青,梁昌勇,等.交互式多智能体进化算法及其应用.系统仿真学报, 2006, 18(7): 2030-2032, 2055)

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