AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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VIEW PDF: A GENETIC ALGORITHM APPROACH FOR EVOLVING NEURAL NETWORKS
We present an alternative approach for training feed-forward neural networks (abbrev. NN) by means of a genetic algorithm (abbrev. GA) that alters the network’s hidden weights and biases. We, by no means, out-rule the back-propagation training algorithm, but instead use it to train the evolved NNs for a much smaller number of generations and focus more on the mutation and crossover operators and how they can be applied.The basic principle involved is that each and every NN can be treated as a chromosome for a GA and, as a consequence, is subject to the mutation and crossover operators. A notable advantage of our approach is that we not only avoid over-fitting the NN, but are also able to alter the number of hidden neurons that make up the hidden layer of the NN, effectively removing the need for the user to specify them explicitly. We manage to outperform plain-vanilla NNs by a factor of 1 to 10 percent on well-known data sets. At first this may not seem significant, but it becomes crucial when dealing with applications where accuracy is critical and training time is not an issue (such as disease diagnosis).
2010 Mathematics Subject Classification. 92B20.1998 CR Categories and Descriptors. A.1 General Literature [INTRODUCTORYAND SURVEY]; I.2.6 Computing Methodologies [ARTIFICIAL INTELLIGENCE]:Learning {Connectionism and neural nets.
Key words and phrases. biology, AI, neural network, genetic algorithm, evolution, mutation, crossover, optimization.
