An Estimation of Distribution Algorithm With Filtering and Learning

Abstract

Estimation of distribution algorithm (EDA) is an efficient population-based stochastic search technique. Since it was proposed, many attempts have been made to improve its performance in the context of non-linear continuous optimization. However, the success of EDA depends on the accuracy of modeling, the effectiveness of sampling and the ability of exploration. An effective EDA often needs to take some measures to adjust the model and to guide sampling. In this paper, we propose a novel estimation of distribution algorithm which applies the idea of Kalman filtering to revise the modeling data and a learning strategy to improve sampling. The filtering scheme modifies the modeling data set using an estimation error matrix based on historic solution data. During the sampling process, the learning strategy determines the region to sample next based on the sampling outcomes so far, instead of completely random sampling. The proposed EDA also employs a multivariate probabilistic model based on copula function and can quickly reach the promising area in which the optimal solution is likely to be located. A collection of general benchmark functions are used to test the performance of the proposed algorithm. Computational experiments show that the EDA is effective.

Prediction of Molten Iron Temperature in the Transportation Process of Torpedo Car

“…where α i is the Lagrangian multiplier and 0 < α i ≤ C. In this paper, to obtain a more accurate data analytics model based on SVM, C and γ are optimized by EDA. 19) In addition, in the process of model training, the EDA-SVM is used to realize three classification problems. In order to realize the above method, we design an offline and on-line system for data processing and model parameters updating.…”