CAREER: Search-Based Optimization of Combinatorial Structures via Expensive Experiments

职业：通过昂贵的实验进行基于搜索的组合结构优化

基本信息

批准号：
1845922
负责人：
Janardhan Rao Doppa
金额：
$ 54.97万
依托单位：
Washington State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845922&HistoricalAwards=false
关键词：
CAREER Search Based Optimization Combinatorial

项目摘要

Many design-optimization problems in science and engineering applications involve performing experiments that are expensive in terms of the consumed resources (computational or physical). These experiments are often guided by intuition and performed by human engineers and scientists in an series of investigations informed by results of prior experiments. This experimental design process can be very challenging when the design space is combinatorial with rich structure among the design variables (e.g., sets, sequences, and graphs). This five-year project is an integrated research, education, and outreach program focused on transforming the practice of optimizing combinatorial design spaces by developing new artificial intelligence (AI) based algorithms for such experiments. The research goal of this project is to develop a new search-based learning and optimization framework to address the challenges associated with optimizing combinatorial design spaces consisting of discrete and hybrid (mixture of discrete and continuous design variables) structures. This framework tightly integrates advances in machine learning and AI search to intelligently explore the design space by reasoning about the available resource budget and the usefulness of potential information the experiments may provide. The search-based framework will be extended to two novel settings towards the goal of improving the resource-efficiency for design optimization. First, the side-information generated by the experiments will be modeled and exploited appropriately. Second, multi-fidelity experiments that trade off accuracy and consumed resources will be leveraged based on their availability. The project will apply the developed algorithms to revolutionize the areas of electronic design automation, design of materials, and design of synthetic microbiomes via close collaboration with domain experts from these application areas. The techniques developed in this project will be made available to academia and industry through open-source software. Results will be disseminated widely through research papers, conference presentations, tutorials, and short courses to maximize the benefits to the scientific community. Educational and outreach activities will include a novel Ambassador program to improve the interest of community college students including under-represented minorities in computer science careers; involving undergraduate students in research projects; a short summer-course on data-driven design optimization for engineers and scientists at WSU; and recruiting and mentoring under-represented minority groups in computer science and engineering through an existing program called LSAMP at Washington State University.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

科学和工程应用中的许多设计优化问题都涉及执行在消耗资源（计算或物理）方面昂贵的实验。这些实验通常以直觉为指导，并由人类工程师和科学家进行一系列调查，这些研究以先前的实验结果告知。当设计空间与设计变量（例如集合，序列和图形）之间的丰富结构相结合时，这个实验设计过程可能非常具有挑战性。这个五年的项目是一项综合研究，教育和外展计划，致力于通过开发基于新的人工智能（AI）算法来改造组合设计空间的实践。该项目的研究目标是开发一个新的基于搜索的学习和优化框架，以应对与优化组合设计空间相关的挑战，该组合设计空间由离散和混合（离散和连续设计变量）结构组成。该框架紧密整合了机器学习和AI搜索的进步，以通过推理可用的资源预算以及实验可能提供的潜在信息的有用性来智能探索设计空间。基于搜索的框架将扩展到两个新颖的设置，以提高设计优化的资源效率。首先，将适当地对实验产生的侧信息进行建模和利用。其次，将根据其可用性来利用多余的实验，以权衡准确性和消耗资源。该项目将通过与这些应用领域的域专家进行密切合作，将开发的算法应用于彻底改变电子设计自动化，材料设计以及合成微生物组的设计领域。该项目中开发的技术将通过开源软件提供给学术界和行业。结果将通过研究论文，会议演讲，教程和短期课程来广泛传播，以最大程度地利用科学界的好处。教育和外展活动将包括一项新颖的大使计划，以提高社区大学生的兴趣，包括代表性不足的计算机科学职业；让本科生参与研究项目； WSU的工程师和科学家的数据驱动设计优化的简短夏季课程；以及通过现有的华盛顿州立大学LSAMP计划在计算机科学和工程领域的招聘和指导，这一奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的审查标准通过评估来通过评估来支持的。