AutoOED: Automated Optimal Experiment Design Platform
This platform addresses the need for time- and data-efficient multi-objective optimization in machine learning research and applications, though it is incremental as it builds on existing Bayesian optimization methods.
The authors tackled the problem of accelerating optimal solution discovery by developing AutoOED, an automated platform for optimal experiment design that uses multi-objective Bayesian optimization algorithms, resulting in an open-source tool with a GUI and distributed system for efficient and accessible experimentation.
We present AutoOED, an Optimal Experiment Design platform powered with automated machine learning to accelerate the discovery of optimal solutions. The platform solves multi-objective optimization problems in time- and data-efficient manner by automatically guiding the design of experiments to be evaluated. To automate the optimization process, we implement several multi-objective Bayesian optimization algorithms with state-of-the-art performance. AutoOED is open-source and written in Python. The codebase is modular, facilitating extensions and tailoring the code, serving as a testbed for machine learning researchers to easily develop and evaluate their own multi-objective Bayesian optimization algorithms. An intuitive graphical user interface (GUI) is provided to visualize and guide the experiments for users with little or no experience with coding, machine learning, or optimization. Furthermore, a distributed system is integrated to enable parallelized experimental evaluations by independent workers in remote locations. The platform is available at https://autooed.org.