Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks

Building Subcomponents in the Cooperative Coevolution Framework for Training Recurrent Neural Networks: Technical Report

Cooperative coevolution decomposes a large problem into its subcomponents and uses evolutionary algorithms for solving them in order to gradually solve the large problem. This paper uses cooperative coevolution framework for training recurrent neural networks for grammatical inference problems. In the past, different encoding schemes were used to build subcomponents from the neural network for ...

Cooperative Co-Evolving Neural Networks for Robosoccer Simulation

Encoding subcomponents in cooperative co-evolutionary recurrent neural networks

Cooperative coevolution employs evolutionary algorithms to solve a high-dimensional search problem by decomposing it into low-dimensional subcomponents. Efficient problem decomposition methods or encoding schemes group interacting variables into separate subcomponents in order to solve them separately where possible. It is important to find out which encoding schemes efficiently group separabil...

Memetic cooperative coevolution of Elman recurrent neural networks

Cooperative coevolution decomposes an optimisation problem into subcomponents and collectively solves them using evolutionary algorithms. Memetic algorithms provides enhancement to evolutionary algorithms with local search. Recently, the incorporation of local search into a memetic cooperative coevolution method has shown to be efficient for training feedforward networks on pattern classificati...

Novel Radial Basis Function Neural Networks based on Probabilistic Evolutionary and Gaussian Mixture Model for Satellites Optimum Selection

In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. The main idea is concerning the various strategies to optimize the procedure of Gradient ...