Great deluge algorithm: Difference between revisions

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The '''Great Delugedeluge algorithm''' ('''GD''') is a generic algorithm applied to [[Optimization (mathematics)|optimization]] problems. It is similar in many ways to the [[hill-climbing]] and [[simulated annealing]] algorithms.

The name comes from the analogy that in a great deluge a person climbing a hill will try to move in any direction that does not get his/her feet wet in the hope of finding a way up as the water level rises.

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A new approximate solution '' S' '', called a neighbour of ''S'', is calculated based on ''S''. The badness of '' S' '', '' b' '', is computed and compared with the tolerance. If '' b' '' is better than tolerance, then the algorithm is recursively restarted with ''S'' : = '' S' '', and ''tolerance'' := ''decay(tolerance)'' where ''decay'' is a function that lowers the tolerance (representing a rise in water levels). If '' b' '' is worse than tolerance, a different neighbour '' S* '' of ''S'' is chosen and the process repeated. If all the neighbours of ''S'' produce approximate solutions beyond ''tolerance'', then the algorithm is terminated and ''S'' is put forward as the best approximate solution obtained.

==See also==

* [[:de:Gunter Dueck|de:Gunter Dueck]]

==References==

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* Gunter Dueck: "New Optimization Heuristics The Great Deluge Algorithm and the Record-to-Record Travel", Journal of Computational Physics, Volume 104, Issue 1, p. 86-92, 1993

[[Category:Optimization algorithms and methods]]

==See also==

* [[:de:Gunter Dueck|de:Gunter Dueck]]

[[Category:Optimization algorithms]]

{{Optimization algorithms}}

[[de:Sintflutalgorithmus]]