CAREER: Design and Application of Scalable Hierarchical Optimization Algorithms by Combining Evolutionary Computation, Machine Learning and Statistics

职业：结合进化计算、机器学习和统计学的可扩展分层优化算法的设计和应用

• 批准号: 0547013
• 负责人: Martin Pelikan
• 金额: $ 40万
• 依托单位: University of Missouri-Saint Louis
• 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
• 起止时间: 2006-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0547013&HistoricalAwards=false
• 关键词: CAREER; Design; Application; Scalable; Hierarchical

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.

摘要提案编号：0547013提案题目：职业：结合进化计算、机器学习和统计学设计和应用可扩展的分层优化算法PI姓名： PI机构：密苏里州圣路易斯大学。非凸优化的挑战仍然是最基本的挑战之一，一次又一次地出现在工程的所有分支。这对于理解哺乳动物大脑和各种复杂系统中的创造力机制（打破局部最小值或常规的能力）至关重要。基于遗传算法、运筹学等的现有方法被广泛使用，但在扩展到大的、复杂的问题方面存在困难，部分原因是它们甚至没有解决如何随着人们在任何设计领域获得更多经验而学习更好地搜索。这个PI是少数几个研究人员之一，他们采用了一种新的方法来完成这项任务，称为分布估计算法（EDA）。他开发了新的，有原则的方法，能够从搜索到搜索学习，对于所有设计选择本质上都是离散的情况。在这里，他将把工作扩展到连续变量和网络设计的案例，并使用真实世界的测试平台，探索扩展到更大问题的方法。更广泛的好处：PI将展示他的新方法对药物设计，生物信息学和医学诊断的价值。他还将展示新的扩展如何提高由USC/ISI，Cycorp，Intel，Lockheed，Martin MS 2，MIT，Grumman和斯坦福大学组织的CEONIC项目的好处，该项目已经将离散版本作为国防部应用程序智能认知引擎的一部分。将建立新的跨学科课程，建立在这种新方法所提供的知识的统一基础上。将有新的竞争和推广K-12计划，并在这所大学创建一个新的实验室。