CAREER: Design and Application of Scalable Hierarchical Optimization Algorithms by Combining Evolutionary Computation, Machine Learning and Statistics
CAREER: Design and Application of Scalable Hierarchical Optimization Algorithms by Combining Evolutionary Computation, Machine Learning and Statistics
批准号:
0547013
负责人:
Martin Pelikan
金额:
$40.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-06-01 至 2012-05-31
中文摘要
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英文摘要
AbstractProposal Number: 0547013Proposal Title: CAREER: Design and Application of Scalable Hierarchical Optimization Algorithms by Combining Evolutionary Computation, Machine Learning and StatisticsPI Name: Pelikan, MartinPI Institution: University of Missouri Saint LouisIntellectual Merit. The challenge of nonconvex optimization remains one of the most fundamental challenges occurring again and again in all branches of engineering. It is crucial to understanding the mechanisms for creativity (ability to break out of a local minimum or rut) in the mammalian brain, and in complex systems of all kinds. Existing methods based on genetic algorithms, operations research and the like are widely used, but have difficulties in scaling up to large, complex problems in part because they do not even address how it is possible to learn to search better as one acquires more experience in any design domain. This PI is one of the few researchers taking a new approach to this task, called Estimation of Distribution Algorithms (EDA). He has developed new, principled methods capable of learning from search to search, for the case where all the design choices are discrete in nature. Here he will extend the work to the case of continuous variables and network design, with real-world testbeds, and explore ways to scale up to larger problems.Broader Benefits: The PI will demonstrate the value of his new methods to drug design, bioinformatics and medical diagnostics. He will also show how the new extensions improve the benefits to the CEARCH project organized by USC/ISI, Cycorp, Intel, Lockheed, Martin MS2, MIT, Grumman and Stanford, which is already using the discrete versions as part of an Intelligent Cognitive Engine for DOD applications. New cross-disciplinary courses will be built, building on the unification of knowledge offered by this new approach. There will be new competitions for and outreach to K-12 programs, and a new laboratory created at this university.
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