BASIL用贝叶斯方法优化实验流程，支持多目标自动调参。
BASIL: Bayesian Application for Scientific Iteration and Learning
- 基于贝叶斯框架，结合自定义采集函数实现智能搜索
- 可处理单目标与多目标优化问题，支持历史数据复用
- 提供图形界面和预设模型模板，适合科研人员快速上手
我们提出BASIL，一款面向实验优化的桌面应用。BASIL采用贝叶斯方法，内置专用采集函数，可解决单目标与多目标优化问题。用户可通过图形界面输入实验参数、优化目标及历史数据，系统构建代理模型并结合采集函数指导过程优化。为便于建模，BASIL提供多种预设代理模型模板，适用于任意具有已知输入变量、定义输出与用户指定优化目标的实验或流程。
原文摘要 · Abstract (English)
We introduce BASIL, a user-friendly desktop application for process optimization. BASIL employs a Bayesian approach, incorporating special acquisition functions that can be used to solve both single and multi-objective optimization problems. It provides a graphical interface that enables users to input their experimental parameters, optimization objectives, and legacy data. This is then used to build surrogate models, which are coupled with acquisition functions to guide and optimize a process towards a desired objective. To facilitate model building, BASIL provides a variety of predefined surrogate model templates. BASIL can be used to optimize any arbitrary experiment or process with known, user-defined input variables, optimization objectives, and defined output.
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