A Time-Sensitive System for Black-Box Combinatorial Optimization

When faced with a combinatorial optimization problem, practitioners often turn to black-box search heuristics such as simulated annealing and genetic algorithms. In black-box optimization, the problem-specific components are limited to functions that (1) generate candidate solutions, and (2) evaluate the quality of a given solution. A primary reason for the popularity of black-box optimization is its ease of implementation. The basic simulated annealing search algorithm can be implemented in roughly 30–50 lines of any modern programming language, not counting the problem-specific local-move and cost-evaluation functions. This search algorithm is so simple that it is often rewritten from scratch for each new application rather than being reused.