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

科学和工程应用中的许多设计优化问题涉及执行在消耗资源（计算或物理）方面昂贵的实验。这些实验通常由直觉指导，由人类工程师和科学家根据先前实验的结果进行一系列调查。当设计空间与设计变量（如集合、序列和图形）之间的丰富结构相结合时，这种实验性设计过程可能非常具有挑战性。这个为期五年的项目是一个综合研究、教育和推广计划，重点是通过开发新的基于人工智能（AI）的实验算法来改变优化组合设计空间的实践。该项目的研究目标是开发一种新的基于搜索的学习和优化框架，以解决与优化由离散和混合（离散和连续设计变量的混合）结构组成的组合设计空间相关的挑战。该框架紧密集成了机器学习和人工智能搜索的进展，通过推理可用资源预算和实验可能提供的潜在信息的有用性，智能地探索设计空间。基于搜索的框架将扩展到两个新的设置，以提高设计优化的资源效率。首先，将对实验产生的侧信息进行建模和适当利用。其次，权衡准确性和消耗资源的多保真度实验将基于它们的可用性加以利用。该项目将通过与这些应用领域的专家密切合作，将开发的算法应用于电子设计自动化、材料设计和合成微生物组设计等领域。在这个项目中开发的技术将通过开源软件提供给学术界和工业界。研究结果将通过研究论文、会议报告、教程和短期课程广泛传播，以最大限度地造福科学界。教育和推广活动将包括一项新的大使计划，以提高社区大学生（包括代表性不足的少数族裔）对计算机科学职业的兴趣；让本科生参与研究项目；面向华盛顿州立大学工程师和科学家的数据驱动设计优化短期暑期课程；并通过华盛顿州立大学现有的一个名为LSAMP的项目，在计算机科学和工程领域招募和指导代表性不足的少数群体。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.