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

Washington State University, Pullman WA

Investigators

Abstract

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.