Online Learning In Estimation Of Distribution Algorithms For Dynamic Environments

In this paper, we propose an estimation of distribution algorithm based on an inexpensive Gaussian mixture model with online learning, which will be employed in dynamic optimization. Here, the mixture model stores a vector of sufficient statistics of the best solutions, which is subsequently used to obtain the parameters of the Gaussian components. This approach is able to incorporate into the current mixture model potentially relevant information of the previous and current iterations. The online nature of the proposal is desirable in the context of dynamic optimization, where prompt reaction to new scenarios should be promoted. To analyze the performance of our proposal, a set of dynamic optimization problems in continuous domains was considered with distinct levels of complexity, and the obtained results were compared to the results produced by other existing algorithms in the dynamic optimization literature. © 2011 IEEE.6269Jin, Y., Branke, J., Evolutionary optimization in uncertain environments - A survey (2005) IEEE Transactions on Evolutionary Computation, 9 (3), pp. 303-317. , DOI 10.1109/TEVC.2005.846356Tinos, R., Yang, S., A self-organizing random immigrants genetic algorithm for dynamic optimization problems (2007) Genetic Programming and Evolvable Machines, 8 (3), pp. 255-286. , DOI 10.1007/s10710-007-9024-zYang, S., Yao, X., Population-based incremental learning with associative memory for dynamic environments (2008) Evolutionary Computation, IEEE Transactions on, 12 (5), pp. 542-561Mendes, R., Kennedy, J., Neves, J., The fully informed particle swarm: Simpler, maybe better (2004) Evolutionary Computation, IEEE Transactions on, 8 (3), pp. 204-210. , JuneLi, X., Branke, J., Blackwell, T., Particle swarm with speciation and adaptation in a dynamic environment (2006) GECCO 2006 - Genetic and Evolutionary Computation Conference, 1, pp. 51-58. , GECCO 2006 - Genetic and Evolutionary Computation ConferenceDe França, F., Von Zuben, F., De Castro, L., An artificial immune network for multimodal function optimization on dynamic environments (2005) Proceedings of the 2005 Conference on Genetic and Evolutionary Computation. 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A Multi-gaussian Component Eda With Restarting Applied To Direction Of Arrival Tracking

This paper analyzes the application of a multi-population Gaussian-based estimation of distribution algorithm equipped with a restarting strategy and mutation, named MGcEDA, to the problem of estimating the Direction of Arrival (DOA) of time-varying plane waves impinging on a uniform linear array of sensors. This problem requires the minimization of a dynamic cost function which is non-linear, non-quadratic, multimodal and variant with respect to the signal-to-noise ratio. Experiments showed that MGcEDA was able to quickly respond to changes in the source features in scenarios with different levels of noise and number of signals. Moreover, MGcEDA outperforms a previously proposed approach in all considered experiments in terms of well known performance measures. © 2013 IEEE.15561563Bacardit, J., Stout, M., Hirst, J., Valencia, A., Smith, R., Krasnogor, N., Automated alphabet reduction for protein datasets (2009) BMC Bioinformat-ics, 10 (1), p. 6Badidi, L., Radouane, L., A neural network approach for doa estimation and tracking (2000) Statistical Signal and Array Processing 2000. 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Studies in Computational Intelligence, 33, pp. 275-289. , M. Pelikan, K. Sastry, and E. CantúPaz, Eds Springer Berlin HeidelbergYuan, B., Orlowska, M., Sadiq, S., Extending a class of continuous estimation of distribution algorithms to dynamic problems (2008) Optimiza-tion Letters, 2 (3), pp. 433-44