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J4  2013, Vol. 47 Issue (10): 1697-1704    DOI: 10.3785/j.issn.1008-973X.2013.10.001
    
Quantification-I theory based IGA and its application
浙江大学 CAD&CG国家重点实验室,浙江 杭州 310027
State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310027, China
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Abstract  

Aiming at solving the problem of user fatigue in interactive genetic algorithm (IGA), the quantification-I theory was introduced to improve the IGA performance in the aspects of enhancing the convergence speed and simulating the manual fitness evaluation. The uniform design method was utilized to guide the generation of initial population, which can make the gene unit types appear evenly and improve global optimal solution convergence performance of IGA. The contribution weights of gene unit types were computed for fitness evaluation based on the quantification-I theory. Then the GA operations of selection, crossover, and mutation were guided to speed the IGA convergence using these weights information. At the later period of IGA, the weights of gene unit types were used to simulate the manual fitness evaluation for reducing the user fatigue and improving efficiency. The method was applied in three-dimensional toy morphological modeling. Results show that the solution can effectively enhance the convergence speed and reduce the user fatigue in IGA.



Published: 01 October 2013
CLC:  TP 391  
Cite this article:

WANG Jin, LU Guo-dong, ZHANG Yun-long. Quantification-I theory based IGA and its application. J4, 2013, 47(10): 1697-1704.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2013.10.001     OR     http://www.zjujournals.com/eng/Y2013/V47/I10/1697


基于数量化一类分析的IGA算法及应用

针对交互式遗传算法(IGA)中用户易疲劳的问题,引入数量化一类分析方法,从提高收敛速度和模拟人工评价两方面入手,改善IGA性能.采用均匀设计法确定遗传算法的初始种群,使得各基因单元类型在初始种群中均匀分布,从而提高遗传算法全局最优解的收敛性能;利用数量化一类分析,求取各基因单元类型对适应度评价的贡献权值,指导GA选择、交叉、变异等操作,以加速算法收敛;在GA操作后期,利用各基因单元类型对适应度评价的权值,模拟个体适应度的自动评价,降低用户疲劳度和提高效率.将该方法应用于动漫玩具形态造型中.结果表明,采用该方法可以有效地提高收敛速度和降低用户疲劳度.

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