A fast learning neuroevolutionary algorithm for both feedforward and recurrent networks is
proposed. The method is inspired by the well known and highly effective Cartesian genetic …

Neuroevolution, the application of evolutionary algorithms to artificial neural networks
(ANNs), is well-established in machine learning. Cartesian Genetic Programming (CGP) is a …

Genetic micro programming of neural networks | Advances in genetic programming skip to
main content ACM Digital Library home ACM home Google, Inc. (search) Advanced Search …

Artificial Neural Networks (ANN) are generally used as universal optimizers [1]. Given a set
of input and output values of a specific problem, no matter how complex the problem is, an …

A novel representation of Recurrent Artificial neural network is proposed for non-linear
markovian and non-markovian control problems. The network architecture is inspired by …

Artificial neural network (ANN) introduces different types of neural network structures and
has been applied successfully in diverse domains of real-world problems. Among various …

Artificial Neural Network (ANN) design is a complex task because its performance depends
on the architecture, the selected transfer function, and the learning algorithm used to train …

Genetic algorithms are a robust adaptive optimization method based on biological
principles. A population of strings representing possible problem solutions is maintained …

Abstract In this paper, Parallel Evolutionary Algorithms for integer weightneural network
training are presented. To this end, each processoris assigned a subpopulation of potential …

This paper presents a neurogenetic learning algorithm which is an integrated method of
designing and training neural networks using genetic algorithms. The proposed scheme …