- author = "Andreas Bolte and Ulrich Wilhelm Thonemann",
- title = "Optimizing Simulated Annealing Schedules with Genetic Programming",
- journal = "European Journal of Operational Research",
- year = "1996",
- volume = "92",
- number = "2",
- pages = "402--416",
- month = "19 " # jul,
- keywords = "genetic algorithms, genetic programming, Optimization, Simulated annealing, Quadratic assignment problem",
-
URL = "
http://www.sciencedirect.com/science/article/B6VCT-3VW8NPR-14/2/d6032805608b3a86412054ccde16f0e6",
- ISSN = "0377-2217",
-
DOI = "
10.1016/0377-2217(94)00350-5",
-
abstract = "Combinatorial optimisation 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 optimisation 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.",
-
notes = "
",
