非平稳环境中的子结构小生境
Sub-Structural Niching in Non-Stationary Environments
摘要：
小生境使遗传算法能够保持种群的多样性。当问题有多个最优解时，它特别有用，目标是找到所有或尽可能多的这些最优解。当问题的适应度景观随时间变化时，该问题称为非平稳、动态或时变问题。在这些问题中，小生境可以保持有用的解决方案，以快速，可靠和准确地响应环境的变化。在本文中，我们提出了一个小生境方法，工作在问题的子结构，而不是整个解决方案，因此它具有较低的空间复杂度比以前已知的小生境机制。我们表明，该方法是准确地响应时，环境变化发生。
英文摘要：
Niching enables a genetic algorithm (GA) to maintain diversity in a population. It is particularly useful when the problem has multiple optima where the aim is to find all or as many as possible of these optima. When the fitness landscape of a problem changes overtime, the problem is called non--stationary, dynamic or time--variant problem. In these problems, niching can maintain useful solutions to respond quickly, reliably and accurately to a change in the environment. In this paper, we present a niching method that works on the problem substructures rather than the whole solution, therefore it has less space complexity than previously known niching mechanisms. We show that the method is responding accurately when environmental changes occur.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
