Modeling optimization algorithm runtime behavior and its applications

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Abstract
We model the time-quality relationship of optimization processes by either fitting curves or training artificial neural networks. On the example of the MAX-SAT problem, we investigate 1) the interpretation of fitted curves based on the values of their parameters using their fixed semantics, 2) the classification of performance measurements to algorithms, i.e., the detection of which algorithm was used to solve a given problem just by its runtime behavior, 3) the prediction of how an algorithm may perform on a yet-unseen problem with yet-unseen features based on its performance on other problems, and 4) the prediction of future progress based on models fitted to data measured so far.
Funding Information
  • National Natural Science Foundation of China (61673359, 61150110488, 71520107002)
  • Fundamental Research Funds for the Central Universities

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