论文标题
时间异质性改善遗传算法的速度和收敛性
Temporal Heterogeneity Improves Speed and Convergence in Genetic Algorithms
论文作者
论文摘要
近几十年来，遗传算法已被用于解决各种各样的搜索问题。这些算法模拟了自然选择，以探索参数空间，以寻找解决方案的各种问题。在本文中，我们探讨了在遗传算法中引入时间异质性的影响。特别是，我们将交叉概率设定为与个人的健康状况成反比，即，更好的解决方案的变化速度比适合度较低的解决方案慢。作为案例研究，我们应用异质性来解决$ n $ Queens和旅行销售人员问题。我们发现时间异质性始终如一地改善搜索，而没有事先了解参数空间。
Genetic algorithms have been used in recent decades to solve a broad variety of search problems. These algorithms simulate natural selection to explore a parameter space in search of solutions for a broad variety of problems. In this paper, we explore the effects of introducing temporal heterogeneity in genetic algorithms. In particular, we set the crossover probability to be inversely proportional to the individual's fitness, i.e., better solutions change slower than those with a lower fitness. As case studies, we apply heterogeneity to solve the $N$-Queens and Traveling Salesperson problems. We find that temporal heterogeneity consistently improves search without having prior knowledge of the parameter space.
