Finding improved simulated annealing schedules with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Thonemann:1994:SAGP,

• author = "Ulrich Wilhelm Thonemann",
• title = "Finding improved simulated annealing schedules with genetic programming",
• booktitle = "Proceedings of the 1994 IEEE World Congress on Computational Intelligence",
• year = "1994",
• volume = "1",
• pages = "391--395",
• address = "Orlando, Florida, USA",
• month = "27-29 " # jun,
• publisher = "IEEE Press",
• DOI = "doi:10.1109/ICEC.1994.349919",
• keywords = "genetic algorithms, genetic programming, simulated annealing, quadratic assignment problem QAP, combinatorial optimisation problem, heuristics, optimal annealing schedule, performance, quadratic assignment problem, combinatorial mathematics, optimisation, scheduling simulated annealing",
• size = "5 pages",
• abstract = "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 optimising the annealing schedule for one of the hardest combinatorial optimisation 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",
• notes = "Uses GP to generate cooling schedule for simulated annealing. Demonstrates this on a series of QAP and compares very favourably with published QAP results. GP fitness found by running simulated annealing, so end up doing loads of work. Best cooling schedules found are problem dependant but several are highly oscillatory and most don't drop to zero!",

}

Genetic Programming entries for Ulrich Thonemann

Citations