Chaining of Numerical Black-box Algorithms: Warm-Starting and Switching Points
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Dynamic algorithm selection can be beneficial for solving numerical black-box problems, in which we implement an online switching mechanism between optimization algorithms. In this approach, we need to decide when a switch should take place and which algorithm to pick for the switching. Intuitively, this approach chains the algorithms for combining the well-performing segments from the performance profile of the algorithms. To realize efficient chaining, we investigate two important aspects - how the switching point influences the overall performance and how to warm-start an algorithm with information stored in its predecessor. To delve into those aspects, we manually construct a portfolio comprising five state-of-the-art optimization algorithms and only consider a single switch between each algorithm pair. After benchmarking those algorithms with the BBOB problem set, we choose the switching point for each pair by maximizing the theoretical performance gain. The theoretical gain is compared to the actual gain obtained by executing the switching procedure with the corresponding switching point. Moreover, we devise algorithm-specific warm-starting methods for initializing the algorithm after the switching with the information learned from its predecessor. Our empirical results show that on some BBOB problems, the theoretical gain is realized or even surpassed by the actual gain. More importantly, this approach discovers a chain that outperforms the single best algorithm on many problem instances. Also, we show that a proper warm-starting procedure is crucial to achieving high actual performance gain for some algorithm pairs. Lastly, with a sensitivity analysis, we find the actual performance gain is hugely affected by the switching point, and in some cases, the switching point yielding the best actual performance differs from the one computed from the theoretical gain.
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