Great deluge algorithm: Difference between revisions

Revision as of 03:54, 19 March 2008

Revision as of 03:55, 19 March 2008

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The '''Great Deluge 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 up the hill in any direction that does not get his/her feet wet in the hope of finding a way up as the water level rises.

In a typical implementation of the GD, the algorithm starts with a poor approximation, ''S'', of the optimum solution. A numerical value called the ''badness'' is computed based on ''S'' and it measures how undesirable the initial approximation is. The higher the value of ''badness'' the more undesirable is the approximate solution. Another numerical value called the ''tolerance'' is calculated based on a number of factors, often including the initial badness.