Abstract The fundamental unit of each evolutionary algorithm is the individual. Each individual represents a potential solution to the problem at hand. Despite the importance of individual solution for multi-objective algorithms’ performance the majority of the existing implementations select a simplistic approach by assuming identical behavior for all candidate solutions of a population. However, from the biological analogue we know that individuals do not react similarly to the same stimulus. This is called character and it is lacking from existing implementations. In this paper, we emulate the corresponding human social analogue by generating individuals that exhibit different behavior when are subject to the same stimulus. The implementation of different behaviors is facilitated through a novel mutation operator. The experimental results favor the proposed approach when compared with other state-of-the-art algorithms for a number of test instances.

中文翻译：

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通过在候选解决方案中模拟人类社会类比的行为来提高多目标算法的性能

摘要 每个进化算法的基本单位是个体。每个人都代表了手头问题的潜在解决方案。尽管单个解决方案对于多目标算法的性能很重要，但大多数现有实现选择了一种简单化的方法，假设总体的所有候选解决方案具有相同的行为。然而，从生物类似物我们知道，个体对相同刺激的反应并不相似。这称为字符，现有实现中缺少它。在本文中，我们通过生成在受到相同刺激时表现出不同行为的个体来模拟相应的人类社会类比。通过新的变异算子促进了不同行为的实现。