We explore building generative neural network models of popular reinforcement learning
environments. Our world model can be trained quickly in an unsupervised manner to learn a …

In this paper we introduce a biologically plausible methodology capable to automatically
generate Artificial Neural Networks (ANNs) with optimum number of neurons and adequate …

In recent years, machine learning has become increasingly important in daily life. One of the
most popular machine learning models used in many applications is an Artificial Neural …

In this paper, we explore the integration of evolutionary computation into the development
and run-time support of dynamically-adaptable, high-assurance middleware. The open …

It is shown that the current global crisis can be considered as financial one in the first
approximation only; in fact, its nature is mainly determined by the crisis of ability of society to …

This paper presents an application of evolutionary search procedures to artificial neural
networks. Here, we can distinguish among three kinds of evolution in artificial neural …

In every learning or training environment, exercises are the basis for practical learning.
Learners need to practice in order to acquire new abilities and perfect those gained …

This paper develops three frameworks based on a metaheuristic algorithm to train neural
network classifiers. The architecture is a single‐hidden‐layer feedforward network. The first …

The framework of neuroevolution (NE) provides a way of finding appropriate structures as
well as connection weights of artificial neural networks. However, the conventional NE …

Abstract Machine learning scientists aim to discover techniques that can be applied across
diverse sets of problems. Such techniques need to exploit regularities that are shared across …