Modern Machine Learning for Automatic Optimization Algorithm Selection
نویسندگان
چکیده
Optimization software is commonly used to solve simulation-based problems such as optimal design and control, model parameter estimation, and best/worst-case scenario identification. While the value of such software is widely recognized, user feedback indicates that these tools are difficult for nonexperts to use. In particular, users are unfamiliar with the details of the plethora of available optimization algorithms and thus do not know which algorithm is appropriate for the problem at hand. In this paper, we will present an approach based on modern machine learning techniques for automatically selecting an optimization algorithm to solve a given problem. We will discuss the feature sets used to characterize the optimization problems and algorithms, and we will review the metrics used to evaluate optimization algorithm performance. Finally, we will present the results from our initial studies with the CUTEr optimization test set and discuss the challenges of generalizing the methodology to simulation-based optimization problems.
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