Optimizing neural networks using faster, more accurate genetic search
Proceedings of the third international conference on Genetic algorithms
Improving the performance of genetic algorithms in automated discovery of parameters
Proceedings of the seventh international conference (1990) on Machine learning
Genetic neural networks on MIMD computers
Genetic neural networks on MIMD computers
Learning and evolution in neural networks
Adaptive Behavior
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Parametric Connectivity: Training of Constrained Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Feature Selection for Counterpropagation Networks
Genetic Algorithms for Feature Selection for Counterpropagation Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
A new evolutionary neural network and its application for the extraction of vegetation anomalies
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
| Hi-index | 0.00 |
The FIRST (Faint Images of the Radio Sky at Twenty-cm) survey is an ambitious project scheduled to cover 10,000 square degrees of the northern and southern galactic caps. Until recently, astronomers associated with FIRST identified radio-emitting galaxies with a bent-double morphology through a visual inspection of images. Besides being subjective, prone to error and tedious, this manual approach is becoming increasingly infeasible: upon completion, FIRST will include almost a million galaxies. This paper describes the application of six methods of evolving neural networks (NNs) with genetic algorithms (GAs) to the identification of bent-double galaxies. The objective is to demonstrate that GAs can successfully address some common problems in the application of NNs to classification problems, such as training the networks, choosing appropriate network topologies, and selecting relevant features. We measured the overall accuracy of the networks using the arithmetic and geometric means of the accuracies on bent and non-bent galaxies. Most of the combinations of GAs and NNs perform equally well on our data, but using GAs to select feature subsets produces the best results, reaching accuracies of 90% using the arithmetic mean and 87% with the geometric mean. The networks found by the GAs were more accurate than hand-designed networks and decision trees.