Details for NCL-TR-2008006
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NameNCL-TR-2008006
Description
Sensibility of Linkage Information and Effectiveness of Estimated Distributions
Chuang, Chung-Yao, & Chen, Ying-ping
Abstract: Probabilistic model building performed by estimation of distribution algorithms (EDAs) enables these methods to use advanced techniques of statistics and machine learning for automatic discovery of problem structures. However, in some situations, complete and accurate identification of all problem structures by probabilistic modeling is not possible because of certain inherent properties of the given problem. In this work, we illustrate one possible cause of such situations with problems composed of structures of unequal fitness contributions. Based on the illustrative example, a notion is introduced that the estimated probabilistic models should be inspected to reveal the effective search directions, and we propose a general approach which utilizes a reserved set of solutions to examine the built model for likely inaccurate fragments. Furthermore, the proposed approach is implemented on the extended compact genetic algorithm (ECGA) and experimented on several sets of additively separable problems with different scaling setups. The results indicate that the proposed method can significantly assist ECGA to handle problems comprising structures of disparate fitness contributions and therefore may potentially help EDAs in general to overcome those situations in which the entire structure of the problem cannot be recognized properly due to the temporal delay of emergence of some promising partial solutions.
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Created On: 08/13/2008 12:18
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