论文标题
在太阳辐射估计中的可变选择的不同类型的利基遗传算法的比较
A comparison of different types of Niching Genetic Algorithms for variable selection in solar radiation estimation
论文作者
论文摘要
可变选择问题通常比单个解决方案更重要，有时值得找到尽可能多的解决方案。应用于这种问题的进化算法的使用被证明是找到最佳解决方案的最佳方法之一。此外，有一些变体旨在查找所有或几乎所有局部的Optima，称为NICHING GENETIC算法（NGA）。为了完成此任务，开发了几种不同的NGA方法。目前的工作比较了八种不同的壁画技术的行为，该技术应用于阿根廷图库曼分布的四个气象站的气候数据库。目标是找到通过估计方法用作输入变量的不同输入变量集。根据低估计误差和低分散误差以及大量不同的结果和低计算时间评估最终结果。进行了第二项实验，以研究该方法识别临界变量的能力。最好的结果是通过确定性的拥挤获得。相比之下，在大多数相似和概率的拥挤中，稳态最差显示出良好的结果，但处理时间更长，确定关键因素的能力较小。
Variable selection problems generally present more than a single solution and, sometimes, it is worth to find as many solutions as possible. The use of Evolutionary Algorithms applied to this kind of problem proves to be one of the best methods to find optimal solutions. Moreover, there are variants designed to find all or almost all local optima, known as Niching Genetic Algorithms (NGA). There are several different NGA methods developed in order to achieve this task. The present work compares the behavior of eight different niching techniques, applied to a climatic database of four weather stations distributed in Tucuman, Argentina. The goal is to find different sets of input variables that have been used as the input variable by the estimation method. Final results were evaluated based on low estimation error and low dispersion error, as well as a high number of different results and low computational time. A second experiment was carried out to study the capability of the method to identify critical variables. The best results were obtained with Deterministic Crowding. In contrast, Steady State Worst Among Most Similar and Probabilistic Crowding showed good results but longer processing times and less ability to determine the critical factors.
