摘要翻译：
垂直总电子含量（vTEC）是一个电离层特性，用于推导电离层对近垂直跨电离层链路施加的信号延迟。本文的主要目的是根据影响这一参数在日、季节和长期时间尺度上变化的主要因素，设计一个预测模型。该模型应足够准确和全面，以便有效地逼近VTEC的高变化。然而，良好的近似性和泛化是相互冲突的目标。为此，设计并提出了一种基于分解特征的多目标进化遗传规划(GP)算法（GP-MOEA/D）用于塞浦路斯上空vTEC的建模。实验结果表明，多目标GP模型考虑了11年的vTEC实测数据，对模型参数有较好的逼近，可以作为一个局部模型来考虑电离层对定位的影响。具体而言，GP-MOEA/D方法在大多数情况下优于单目标优化GP、具有非支配排序遗传算法II（NSGA-II）特性的GP和以前提出的基于神经网络的方法。
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英文标题：
《A GP-MOEA/D Approach for Modelling Total Electron Content over Cyprus》
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作者：
Andreas Konstantinidis, Haris Haralambous, Alexandros Agapitos and
  Harris Papadopoulos
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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英文摘要：
  Vertical Total Electron Content (vTEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on near-vertical trans-ionospheric links. The major aim of this paper is to design a prediction model based on the main factors that influence the variability of this parameter on a diurnal, seasonal and long-term time-scale. The model should be accurate and general (comprehensive) enough for efficiently approximating the high variations of vTEC. However, good approximation and generalization are conflicting objectives. For this reason a Genetic Programming (GP) with Multi-objective Evolutionary Algorithm based on Decomposition characteristics (GP-MOEA/D) is designed and proposed for modeling vTEC over Cyprus. Experimental results show that the Multi-Objective GP-model, considering real vTEC measurements obtained over a period of 11 years, has produced a good approximation of the modeled parameter and can be implemented as a local model to account for the ionospheric imposed error in positioning. Particulary, the GP-MOEA/D approach performs better than a Single Objective Optimization GP, a GP with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) characteristics and the previously proposed Neural Network-based approach in most cases.
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PDF链接：
https://arxiv.org/pdf/1111.5720