Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication
Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication
复制标题
时间序列数据规则集发现的混合进化算法预测和解释形态和富营养化不同的两个湖泊中的藻类种群动态
DOI：
10.1007/3-540-28426-5_17
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
N. Takamura
中科院分区：
文献类型：
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作者：
H. Cao;F. Recknagel;B. Kim;N. Takamura
17.4结论混合进化算法（HEA）已被开发用于发现复杂生态数据中的预测规则集。它已被设计成通过使用遗传规划和优化的随机参数的规则集的结构，通过遗传algorithm.HEA被成功地应用于长期监测数据的浅，富营养化的霞浦湖（日本）和深，中营养化的鄱阳湖（韩国）。结果表明，HEA是能够发现的规则集，它可以预测7天前的季节性丰富的蓝藻和硅藻种群在两个湖泊具有相对较高的精度，但也解释了物理，化学变量和藻类种群的丰度之间的关系。对最佳规则集的解释和敏感性分析与以往研究的理论假设和实验结果吻合较好。
17.4 ConclusionsA hybrid evolutionary algorithm (HEA) has been developed to discover predictive rule sets in complex ecological data. It has been designed to evolve the structure of rule sets by using genetic programming and to optimise the random parameters in the rule sets by means of a genetic algorithm.HEA was successfully applied to long-term monitoring data of the shallow, eutrophic Lake Kasumigaura (Japan) and the deep, mesotrophic Lake Soyang (Korea). The results have demonstrated that HEA is able to discover rule sets, which can forecast for 7-days-ahead seasonal abundances of blue-green algae and diatom populations in the two lakes with relatively high accuracy but are also explanatory for relationships between physical, chemical variables and the abundances of algal populations. The explanations and the sensitivity analysis for the best rule sets correspond well with theoretical hypotheses and experimental findings in previous studies.