NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks
(ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting …

Neural networks have proven to be a highly effective tool for solving complex problems in
many areas of life. Recently, their importance and practical usability have further been …

The choice of activation function can have a large effect on the performance of a neural
network. While there have been some attempts to hand-engineer novel activation functions …

Abstract In Deep Neural Networks (DNNs), several architectures had been proposed for the
various complex tasks such as Machine Translation, Natural Language processing and time …

Deep Neural Networks have been shown to be beneficial for a variety of tasks, in particular
allowing for end-to-end learning and reducing the requirement for manual design decisions …

Profiled side-channel analysis (SCA) leverages leakage from cryptographic
implementations to extract the secret key. When combined with advanced methods in neural …

In a multi-objective setting, a portfolio manager's highly consequential decisions can benefit
from assessing alternative forecasting models of stock index movement. The present …

Multiclass classification is a fundamental and challenging task in machine learning. The
existing techniques of multiclass classification can be categorized as (1) decomposition into …

This article presents a novel method, called Modular Grammatical Evolution (MGE), toward
validating the hypothesis that restricting the solution space of NeuroEvolution to modular …

Parameter adaptation is one of the key research fields in the area of evolutionary
computation. In this study, the application of neuroevolution of augmented topologies to …