Using genetic programming for artificial neural network development and simplification
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Evolving Complex Neural Networks
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
An Evolutionary Approach for Tuning Artificial Neural Network Parameters
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Automatic Recurrent ANN development for signal classification: detection of seizures in EEGs
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A Genetic Algorithm for ANN Design, Training and Simplification
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Evolving simple feed-forward and recurrent ANNs for signal classification: a comparison
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Group search optimizer: an optimization algorithm inspired by animal searching behavior
IEEE Transactions on Evolutionary Computation
Evolutionarily optimized features in functional link neural network for classification
Expert Systems with Applications: An International Journal
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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
Avoiding local minima in feedforward neural networks by simultaneous learning
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis
Neural Processing Letters
Multi-policy optimization in self-organizing systems
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
Evolving artificial neural networks using adaptive differential evolution
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Learning in the feed-forward random neural network: A critical review
Performance Evaluation
Estimating classifier performance with genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
A group search optimizer for neural network training
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Evolving feed-forward neural networks through evolutionary mutation parameters
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
A hybrid algorithm for artificial neural network training
Engineering Applications of Artificial Intelligence
Artificial neural network training using a new efficient optimization algorithm
Applied Soft Computing
Towards effective algorithms for intelligent defense systems
CSS'12 Proceedings of the 4th international conference on Cyberspace Safety and Security
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
Evolving multilayer feedforward neural network using adaptive particle swarm algorithm
International Journal of Hybrid Intelligent Systems
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There are numerous combinations of neural networks (NNs) and evolutionary algorithms (EAs) used in classification problems. EAs have been used to train the networks, design their architecture, and select feature subsets. However, most of these combinations have been tested on only a few data sets and many comparisons are done inappropriately measuring the performance on training data or without using proper statistical tests to support the conclusions. This paper presents an empirical evaluation of eight combinations of EAs and NNs on 15 public-domain and artificial data sets. Our objective is to identify the methods that consistently produce accurate classifiers that generalize well. In most cases, the combinations of EAs and NNs perform equally well on the data sets we tried and were not more accurate than hand-designed neural networks trained with simple backpropagation.