Finding improved simulated annealing schedules with genetic programming

Many combinatorial problems are too difficult to be solved optimally, and hence heuristics are used to obtain “good” solutions in “reasonable” time. A heuristic that has been successfully applied to a variety of problems is simulated annealing. However, the performance of simulated annealing strongly depends on the appropriate choice of a key parameter, the annealing schedule. Usually, researchers experiment with a number of manually created annealing schedules and then choose the one that performs best for their algorithms. This work applies genetic programming to replace this manual search. For a given problem, we search for an optimal annealing schedule. We demonstrate the potential of this new approach by optimizing the annealing schedule for one of the hardest combinatorial optimization problem, the quadratic assignment problem. We introduce a new algorithm for solving the quadratic assignment problem that performs extremely well, and we outline properties of good annealing schedules