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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Linking Entropy to Estimation of Distribution Algorithms
verfasst von : Alberto Ochoa, Marta Soto
Erschienen in: Towards a New Evolutionary Computation
Verlag: Springer Berlin Heidelberg
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This chapter presents results on the application of the concept of entropy to estimation of distribution algorithms (EDAs). Firstly, the Boltzmann mutual information curves are introduced. They are shown to contain a lot of information about the difficulty of the functions. Next, a design method of discrete benchmark functions is presented. The newly developed approach allows the construction of both single and random classes of functions that obey a given collection of probabilistic constraints. This application and the next — the construction of low cost search distributions — are based on the principle of maximum entropy. The last proposal is the linear entropic mutation (LEM), an approach that measures the amount of mutation applied to a variable as the increase of its entropy. We argue that LEM is a natural operator for EDAs because it mutates distributions instead of single individuals.