Clustering-based probabilistic model fitting in estimation of distribution algorithms
- Type
- Article
- Citation
- Ieice Transactions on Information and Systems, v.E89D, no.1, pp.381 - 383
- Issued Date
- 2006-01
- Abstract
- An efficient clustering strategy for estimation of distribution algorithms (EDAs) is presented. It is used for properly fitting probabilistic models that play an important role in guiding search direction. To this end, a fitness-aided ordering scheme is devised for deciding the input sequence of samples (i.e., individuals) for clustering. It can effectively categorise the individuals by using the (available) information about fitness landscape. Moreover, a virtual leader is introduced for providing a reliable reference for measuring the distance from samples to its own cluster. The proposed algorithm incorporates them within the framework of random the leader algorithm (RLA). Experimental results demonstrate that the proposed approach is more effective than the existing ones with regard to probabilistic model fitting.
- Publisher
- IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
- ISSN
- 1745-1361
- Appears in Collections:
- Department of AI > 1. Journal Articles
Graduate School of AI Policy and Strategy > 1. Journal Articles
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