Optimizing simulated annealing schedules with genetic programming

Combinatorial optimization problems are encountered in many areas of science and engineering. Most of these problems are too difficult to be solved optimally, and hence heuristics are used to obtain ''good'' solutions in reasonable time. One heuristic that has been successfully applied to a variety of problems is Simulated Annealing. The performance of Simulated Annealing depends on the appropriate choice of a key parameter, the annealing schedule. Researchers usually experiment with some manually created annealing schedules and then use the one that performs best in their algorithms. This work replaces this manual search by Genetic Programming, a method based on natural evolution. We demonstrate the potential of this new approach by optimizing the annealing schedule for a well-known combinatorial optimization problem, the Quadratic Assignment Problem. We introduce two new algorithms for solving the Quadratic Assignment Problem that perform extremely well and outperform existing Simulated Annealing algorithms.