In this article, two novel density ensembles methods-the resampling method and the subspaces method-are proposed for enhancing existing continuous Estimation of Distribution Algorithms (EDAs). In resampling continuous EDAs, a population of densities of the selected promising solutions is obtained by iteratively using the resampling operator and the density estimation operator, and new candidate solutions at the next generation are reproduced through sampling from all obtained densities of promising solutions. In subspaces continuous EDAs, a population of densities is obtained by randomly choosing a subset of all variables and estimating the density of all selected high-quality solutions in this subspace. The above steps iterate and many densities of high-quality solutions in different subspaces can be obtained. New candidate solutions at the next generation are reproduced through perturbing the old promising solutions by sampling from the densities in different subspaces. The results upon convergence with different numbers of variables and the effects of parameters on the performance of the density ensembles methods for continuous EDAs are studied based on the experimental results. © 2012 Copyright Taylor and Francis Group, LLC.
Bibliographical noteThis research was supported by a CityU Strategic Research Grant (No. 7002680), the Fundamental Research Funds for the Central Universities, South China University of Technology (Grant No. 2009ZM0081) and the National Natural Science Foundation of China (Grant Nos 10826053 and 70871040).
- Estimation of Distribution Algorithms (EDAs)
- global optimization
- resampling continuous EDAs
- subspaces continuous EDAs