Effective linkage learning using low-order statistics and clustering
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
A framework for multi-model EDAs with model recombination
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
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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.