Add to ORKGEvolutionary computation has been around ever since the late 50s. This thesis aims at elaborate on genetic algorithms, a subset of evolutionary computation, with particular regard to the field of neuroevolution, which is the application of GAs to the generation of functioning neural networks. The most widely adopted techniques are thereby explained and contrasted. The experimentation chapter finally shows an implementation of a genetic algorithm, inspired by existing algorithms, with the objective of optimizing a novel kind of artificial neural network
The design of Artificial Neural Networks by Genetic Algorithm is useful in terms of (1) automating a...
Both genetic programming and neural networks are machine learning techniques that have had a wide ra...
Neuro-genetic systems, a particular type of evolving systems, have become a very important topic of ...
Genetic algorithms are most commonly applied to neural networks to determine their architecture or l...
In this contribution we present a novel method, called Evolutionary Acquisition of Neural Topologies...
This work deals with methods for finding optimal neural network architectures to learn par-ticular p...
Artificial neural networks (ANNs) are applied to many real-world problems, ranging from pattern clas...
Genetic algorithms are computational techniques for search, optimization and machine learning that a...
It is a matter of fact that in Europe evolution strategies and in the U.S.A. genetic algorithms have...
NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial Neural Ne...
Published ArticlePeople have tried different ways to make machines intelligent. One option is to use...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
this article we assume that the reader is familiar with the basic ideas of neural networks but perha...
We develop a tree-based genetic programming system, capable of modelling evolvability during evoluti...
Lately, a lot of research has been conducted on the automatic design of artificial neural networks (...
The design of Artificial Neural Networks by Genetic Algorithm is useful in terms of (1) automating a...
Both genetic programming and neural networks are machine learning techniques that have had a wide ra...
Neuro-genetic systems, a particular type of evolving systems, have become a very important topic of ...
Genetic algorithms are most commonly applied to neural networks to determine their architecture or l...
In this contribution we present a novel method, called Evolutionary Acquisition of Neural Topologies...
This work deals with methods for finding optimal neural network architectures to learn par-ticular p...
Artificial neural networks (ANNs) are applied to many real-world problems, ranging from pattern clas...
Genetic algorithms are computational techniques for search, optimization and machine learning that a...
It is a matter of fact that in Europe evolution strategies and in the U.S.A. genetic algorithms have...
NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial Neural Ne...
Published ArticlePeople have tried different ways to make machines intelligent. One option is to use...
Neural networks and genetic algorithms are the two sophisticated machine learning techniques present...
this article we assume that the reader is familiar with the basic ideas of neural networks but perha...
We develop a tree-based genetic programming system, capable of modelling evolvability during evoluti...
Lately, a lot of research has been conducted on the automatic design of artificial neural networks (...
The design of Artificial Neural Networks by Genetic Algorithm is useful in terms of (1) automating a...
Both genetic programming and neural networks are machine learning techniques that have had a wide ra...
Neuro-genetic systems, a particular type of evolving systems, have become a very important topic of ...