Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming

GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms

Evolutionary techniques are one of the most successful paradigms in the field of optimization. In this paper we present a new approach, named GAEDA, which is a new hybrid algorithm based on genetic and estimation of distribution algorithms. The original objective is to get benefits from both approaches. In order to perform an evaluation of this new approach a selection of synthetic optimization...

Estimation of Distribution Algorithms

The Time Adaptive Self Organizing Map for Distribution Estimation

The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...

Hardware Acceleration of EDA Algorithms

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Pruning Neural Networks with Distribution Estimation Algorithms

This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the...