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A commonly used strategy for improving optimization algorithms is to restartthe algorithm when it is believed to be trapped in an inferior part of thesearch space. Building on the recent success of Bet-and-Run approaches forrestarted local search solvers, we introduce an improved generic Bet-and-Runstrategy. The goal is to obtain the best possible results within a given timebudget t using a given black-box optimization algorithm. If no prior knowledgeabout problem features and algorithm behavior is available, the question abouthow to use the time budget most efficiently arises. We propose to first startk>=1 independent runs of the algorithm during an initialization budget t1<t,pausing these runs, then apply a decision maker D to choose 1<=m<=k runs fromthem (consuming t2>=0 time units in doing so), and then continuing these runsfor the remaining t3=t-t1-t2 time units. In previous Bet-and-Run strategies,the decision maker D=currentBest would simply select the run with the best-so-far results at negligible time. We propose using more advanced methods todiscriminate between "good" and "bad" sample runs, with the goal of increasingthe correlation of the chosen run with the a-posteriori best one. We testseveral different approaches, including neural networks trained or polynomialsfitted on the current trace of the algorithm to predict which run may yield thebest results if granted the remaining budget. We show with extensiveexperiments that this approach can yield better results than the previousmethods, but also find that the currentBest method is a very reliable androbust baseline approach.

An Improved Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search