A Model for Analyzing Black-Box Optimization

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Abstract

The design of heuristics for NP-hard problems is perhaps the most active area of research in the theory of combinatorial algorithms. However, practitioners more often resort to local-improvement heuristics such as gradient-descent search, simulated annealing, tabu search, or genetic algorithms. Properly implemented, local-improvement heuristics can lead to short, efficient programs that yield reasonable solutions. Designers of efficient local-improvement heuristics must make several crucial decisions, including the choice of neighborhood and heuristic for the problem at hand. We are interested in developing a general methodology for predicting the quality of local-neighborhood operators and heuristics, given a time budget and a solution evaluation function.