Property | Value |
Name | NCL-TR-2008001 |
Description |
On the Effectiveness of Distributions Estimated by Probabilistic Model Building
Abstract: Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms that capture the likely structure of promising solutions by explicitly building a probabilistic model and utilize the built model to guide the further search. It is presumed that EDAs can detect the structure of the problem by recognizing the regularities of the promising solutions. However, in certain situations, EDAs are unable to discover the entire structure of the problem because the set of promising solutions on which the model is built contains insufficient information for some parts of the problem and renders EDAs incapable of accurate model building. In this work, we firstly propose a general concept that the effectiveness of probabilistic models should be evaluated and verified in EDAs. Based on the concept, we design a practical approach which utilizes a reserved set of individuals to inspect the built model for the fragments that may be inconsistent with the actual problem structure. Furthermore, we provide an implementation of the designed approach on the extended compact genetic algorithm (ECGA) and conduct numerical experiments. The results indicate that the proposed concept can significantly assist ECGA to handle problems of different scalings. |
Filename | NCL-TR-2008001.pdf |
Filesize | 247.41 kB |
Filetype | pdf (Mime Type: application/pdf) |
Creator | ypchen |
Created On: | 02/02/2008 12:55 |
Viewers | Everybody |
Maintained by | Publisher |
Hits | 3614 Hits |
Last updated on | 12/09/2010 13:42 |
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