摘要翻译:
Lifemapper(http://www.Lifemapper.org)是一个预测地球生物多样性的电子地图集。Lifemapper使用GARP遗传算法的屏保版本来建模物种分布,通过类似于SETI@Home的“志愿者”PC来利用大量计算资源,开发世界动植物分布模型。Lifemapper项目的主要目标是利用关于物种位置的现有数据,提供一个关于物种地图和预测模型(即世界动植物群)的最新和全面的数据库。这些模型是利用分布的博物馆藏品和地理空间环境相关性档案中的样本数据开发的。一个中央服务器维护一个物种地图和模型的动态存档,用于研究、推广到一般社区,并反馈给博物馆数据提供者。本文是一个关于遗传算法在大规模环境信息基础设施开发中的作用、使用和合理性的案例研究。
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英文标题:
《The use of the GARP genetic algorithm and internet grid computing in the
Lifemapper world atlas of species biodiversity》
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作者:
David R.B. Stockwell, James H. Beach, Aimee Stewart, Gregory
Vorontsov, David Vieglais, Ricardo Scachetti Pereira
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最新提交年份:
2005
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分类信息:
一级分类:Quantitative Biology 数量生物学
二级分类:Quantitative Methods 定量方法
分类描述:All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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一级分类:Computer Science 计算机科学
二级分类:Distributed, Parallel, and Cluster Computing 分布式、并行和集群计算
分类描述:Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
包括容错、分布式算法、稳定性、并行计算和集群计算。大致包括ACM学科类C.1.2、C.1.4、C.2.4、D.1.3、D.4.5、D.4.7、E.1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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一级分类:Quantitative Biology 数量生物学
二级分类:Other Quantitative Biology 其他定量生物学
分类描述:Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要:
Lifemapper (http://www.lifemapper.org) is a predictive electronic atlas of the Earth's biological biodiversity. Using a screensaver version of the GARP genetic algorithm for modeling species distributions, Lifemapper harnesses vast computing resources through 'volunteers' PCs similar to SETI@home, to develop models of the distribution of the worlds fauna and flora. The Lifemapper project's primary goal is to provide an up to date and comprehensive database of species maps and prediction models (i.e. a fauna and flora of the world) using available data on species' locations. The models are developed using specimen data from distributed museum collections and an archive of geospatial environmental correlates. A central server maintains a dynamic archive of species maps and models for research, outreach to the general community, and feedback to museum data providers. This paper is a case study in the role, use and justification of a genetic algorithm in development of large-scale environmental informatics infrastructure.
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PDF链接:
https://arxiv.org/pdf/q-bio/0511045