Menczer F, Degeratu M, Street W N
Management Sciences Department, University of Iowa, Iowa City 52242, USA.
Evol Comput. 2000 Summer;8(2):223-47. doi: 10.1162/106365600568185.
Local selection is a simple selection scheme in evolutionary computation. Individual fitnesses are accumulated over time and compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. Local selection, coupled with fitness functions stemming from the consumption of finite shared environmental resources, maintains diversity in a way similar to fitness sharing. However, it is more efficient than fitness sharing and lends itself to parallel implementations for distributed tasks. While local selection is not prone to premature convergence, it applies minimal selection pressure to the population. Local selection is, therefore, particularly suited to Pareto optimization or problem classes where diverse solutions must be covered. This paper introduces ELSA, an evolutionary algorithm employing local selection and outlines three experiments in which ELSA is applied to multiobjective problems: a multimodal graph search problem, and two Pareto optimization problems. In all these experiments, ELSA significantly outperforms other well-known evolutionary algorithms. The paper also discusses scalability, parameter dependence, and the potential distributed applications of the algorithm.
局部选择是进化计算中的一种简单选择方案。个体适应度随时间累积，并与固定阈值进行比较，而非相互比较，以决定谁能进行繁殖。局部选择与源于有限共享环境资源消耗的适应度函数相结合，以类似于适应度共享的方式维持多样性。然而，它比适应度共享更高效，并且适合用于分布式任务的并行实现。虽然局部选择不易过早收敛，但它对种群施加的选择压力最小。因此，局部选择特别适用于帕累托优化或必须涵盖多种解决方案的问题类别。本文介绍了一种采用局部选择的进化算法ELSA，并概述了将ELSA应用于多目标问题的三个实验：一个多模态图搜索问题和两个帕累托优化问题。在所有这些实验中，ELSA显著优于其他著名的进化算法。本文还讨论了该算法的可扩展性、参数依赖性以及潜在的分布式应用。
