Interactive Evolutionary Computation (IEC) is a kind of human–machine interaction calculation method derived from evolutionary computation. The main problem of interactive evolutionary computation is that fitness noise can make evolution direction to deviate from user’s preferences because user’s evaluation has cognitive fluctuations and fatigue. To improve these deficiencies, this paper recommends a fuzzy fitness prediction method based on fuzzy gray model FGM (1,1) with a precise number fitness. First of all, the relationship between fitness noise intensity and the fitness function is proposed. Then, it suggests a linear programming of fuzzy fitness set width under the restriction of minimum noise intensity, which can calculate the fuzzy fitness prediction parameters. Finally, the fuzzy gray model forecasts the fuzzy fitness. The proposed method uses new computation of individual’s dominance relation and crowding distance to realize NSGA–II. The experimental results verify that this method has advantages in improving optimization quality, alleviating user’s fatigue and improving efficiency in exploration.

中文翻译：

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用户偏好的进化计算解决模糊健身预测问题

交互式进化计算（IEC）是一种源自进化计算的人机交互计算方法。交互式进化计算的主要问题是适应度噪声会使进化方向偏离用户的偏好，因为用户的评价存在认知波动和疲劳。针对这些不足，本文推荐了一种基于模糊灰色模型FGM(1,1)的模糊适应度预测方法，具有精确的适应度数。首先提出了适应度噪声强度与适应度函数的关系。然后，提出了在最小噪声强度限制下对模糊适应度集宽度进行线性规划，可以计算出模糊适应度预测参数。最后，模糊灰色模型预测模糊适应度。所提出的方法使用新的个体优势关系和拥挤距离计算来实现NSGA-II。实验结果验证了该方法在提高优化质量、缓解用户疲劳、提高探索效率等方面具有优势。