Add to ORKGAbstract. Living creatures improve their adaptation capabilities to a changing world by means of two orthogonal processes: evolution and lifetime learning. Within Articial Intelligence, both mechanisms in-spired the development of non-orthodox problem solving tools, namely Genetic and Evolutionary Algorithms (GEAs) and Articial Neural Net-works (ANNs). Several local search gradient-based methods have been developed for ANN training, with considerable success; however, in some situations, such procedures may lead to local minima. Under this sce-nario, the combination of evolution and learning techniques, may lead to better results (e.g., global optima). Comparative tests on several Ma-chine Learning tasks attest this claim
In this dissertation the problem of the training of feedforward artificial neural networks and its a...
Supervised training from examples of a feed-forward neural network is a classical problem, tradition...
In this report we present the results of a series of simulations in which neural networks undergo ch...
Living creatures improve their adaptation capabilities to a changing world by means of two orthogona...
Several gradient-based methods have been developed for Artificial Neural Network (ANN) training. Sti...
Abstract—Many frustrating experiences have been encountered when the training of neural networks by ...
Abstract: Classical Machine Learning methods are usually developed to work in static data sets. Yet,...
Artificial Neural Network (ANN) is one of the modern computational methods proposed to solve the maj...
Learning and evolution are two fundamental forms of adaptation. There has been a great interest in c...
. The processes of adaptation in natural organisms consist of two complementary phases: 1) learning,...
: This paper describes two algorithms based on cooperative evolution of internal hidden network repr...
Yang S, Tian Y, He C, Zhang X, Tan KC, Jin Y. A Gradient-Guided Evolutionary Approach to Training De...
Although Artificial Neural Networks (ANNs) are important Data Mining techniques, the search for the ...
Classical Machine Learning methods are usually developed to work in static data sets. Yet, real worl...
Artificial Neural Networks (ANNs) are important Data Mining (DM) techniques. Yet, the search for t...
In this dissertation the problem of the training of feedforward artificial neural networks and its a...
Supervised training from examples of a feed-forward neural network is a classical problem, tradition...
In this report we present the results of a series of simulations in which neural networks undergo ch...
Living creatures improve their adaptation capabilities to a changing world by means of two orthogona...
Several gradient-based methods have been developed for Artificial Neural Network (ANN) training. Sti...
Abstract—Many frustrating experiences have been encountered when the training of neural networks by ...
Abstract: Classical Machine Learning methods are usually developed to work in static data sets. Yet,...
Artificial Neural Network (ANN) is one of the modern computational methods proposed to solve the maj...
Learning and evolution are two fundamental forms of adaptation. There has been a great interest in c...
. The processes of adaptation in natural organisms consist of two complementary phases: 1) learning,...
: This paper describes two algorithms based on cooperative evolution of internal hidden network repr...
Yang S, Tian Y, He C, Zhang X, Tan KC, Jin Y. A Gradient-Guided Evolutionary Approach to Training De...
Although Artificial Neural Networks (ANNs) are important Data Mining techniques, the search for the ...
Classical Machine Learning methods are usually developed to work in static data sets. Yet, real worl...
Artificial Neural Networks (ANNs) are important Data Mining (DM) techniques. Yet, the search for t...
In this dissertation the problem of the training of feedforward artificial neural networks and its a...
Supervised training from examples of a feed-forward neural network is a classical problem, tradition...
In this report we present the results of a series of simulations in which neural networks undergo ch...