Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A RFID Network Planning Method Based on Genetic Algorithm
NSWCTC '09 Proceedings of the 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing - Volume 01
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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Estimation of distribution algorithms are a class of optimization algorithms based on probability distribution model. In this paper, we propose an improved estimation of distribution algorithm using opposition-based learning and Gaussian copulas. The improved algorithm employs multivariate Gaussian copulas to construct probability distribution model and uses opposition-based learning for population initialization and new population generation. By estimating Kendall's tau and using the relationship of Kendall's tau and correlation matrix, Gaussian copula parameters are firstly estimated, thus, joint distribution is estimated. Afterwards, the Monte Carte simulation is used to generate new individuals. Then, the opposite numbers have also been utilized to improve the convergence performances. The improved algorithm is applied to some benchmark functions and optimal placement of readers in RFID networks. The relative experimental results show that the improved algorithm has better performance than original version of estimation of distribution algorithm and is effective in the optimal placement of readers in RFID networks.