Worker ants' rule-based genetic algorithms dealing with changing environments

A. Kamiya, Fumiaki Makino, S. Kobayashi
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引用次数: 5

Abstract

Contrary to popular belief, biologists discovered that worker ants are really not all hardworking. It has been found that in three separate 30-strong colonies of black Japanese ants (Myrmecina nipponica), about 20% of worker ants are diligent, 60% are ordinary, and 20% are lazy. That is called 20:60:20 rule. Though they are lazy, biologists suggested that lazy worker ants could be contributing something to the colony that is yet to be determined. This paper verified that genetic algorithms (GAs) with this worker ants' rule can solve an artificial ant problem efficiently in changing environments. In our approach, for each generation, we preserve not only individuals of high fitness but also individuals of low fitness. As a result of simulation conducted in a changing environment, the best performance of our proposed GA was obtained when the number of preserved individuals of high fitness and low fitness are each close to 20% of the population, while the remaining nearly 60% individuals are created by genetic operations, namely, crossover and mutation. This simulation result reinforces the 20:60:20 rule discovered in nature ant colonies. In a changing environment, this simulation result also indicates that worker ants' rule-based GA outperforms simple GA and CHC.
工蚁基于规则的遗传算法处理不断变化的环境
与普遍的看法相反,生物学家发现工蚁并不都是勤劳的。研究发现,在日本黑蚁(Myrmecina nipponica)的三个独立的30多个蚁群中,大约20%的工蚁是勤奋的,60%是普通的,20%是懒惰的。这就是所谓的20:60:20规则。虽然它们很懒,但生物学家认为,懒惰的工蚁可能对蚁群做出了一些贡献,目前尚不清楚。本文验证了基于该工蚁规则的遗传算法可以有效地解决环境变化中的人工蚂蚁问题。在我们的方法中,对于每一代,我们不仅保留了高适合度的个体,也保留了低适合度的个体。在不断变化的环境中进行模拟,当高适应度和低适应度的个体保存数量分别接近种群的20%时,我们所提出的遗传算法的性能最佳,而剩下的近60%的个体是通过遗传操作,即交叉和突变产生的。这一模拟结果强化了在自然界蚁群中发现的20:60:20规则。在变化的环境中,仿真结果也表明,基于规则的蚁群遗传算法优于简单遗传算法和CHC算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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