Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Designing application-specific neural networks using the genetic algorithm
Advances in neural information processing systems 2
Advances in neural information processing systems 2
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Cooperative-Competitive Algorithms for Evolutionary Networks Classifying Noisy Digital Images
Neural Processing Letters
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
A Learning Algorithm for Evolving Cascade Neural Networks
Neural Processing Letters
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimizing Neural Networks Using FasterMore Accurate Genetic Search
Proceedings of the 3rd International Conference on Genetic Algorithms
Topology Design of Feedforward Neural Networks by Genetic Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Balancing Learning And Evolution
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Genetic Algorithms as a Tool for Restructuring Feature Space Representations
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection
Pattern Recognition Letters
Non-parametric classifier-independent feature selection
Pattern Recognition
A cooperative constructive method for neural networks for pattern recognition
Pattern Recognition
How to shift bias: Lessons from the baldwin effect
Evolutionary Computation
Evolutionary reinforcement learning of artificial neural networks
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
Evolutionary Artificial Neural Network Design and Training for wood veneer classification
Engineering Applications of Artificial Intelligence
Feature subset selection in large dimensionality domains
Pattern Recognition
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
Active guidance for a finless rocket using neuroevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Feature construction and selection using genetic programming and a genetic algorithm
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Evolving neural networks in compressed weight space
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolutionary neural networks for practical applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Enhancing genetic feature selection through restricted search and Walsh analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Orthogonal forward selection and backward elimination algorithms for feature subset selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary optimization of radial basis function classifiers for data mining applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Fuzzy neural networks for classification and detection of anomalies
IEEE Transactions on Neural Networks
Constructive neural-network learning algorithms for pattern classification
IEEE Transactions on Neural Networks
Statistical analysis of the parameters of a neuro-genetic algorithm
IEEE Transactions on Neural Networks
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
Mutation-based genetic neural network
IEEE Transactions on Neural Networks
Fast generic selection of features for neural network classifiers
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Data-Core-Based Fuzzy Min–Max Neural Network for Pattern Classification
IEEE Transactions on Neural Networks - Part 2
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Most methods for the evolutionary generation of multi-layer perceptron classifiers use a divide-and-conquer strategy, where the tasks of feature selection, structure design, and weight training are performed separately. The concurrent evolution of the whole classifier has been seldom attempted and its effectiveness has never been exhaustively benchmarked. This paper presents an experimental study on the merits of this latter approach. Two schemes were investigated. The first method evolves simultaneously the neural network structure and input feature vector, and trains via a standard learning procedure the candidate solutions (wrapper approach). The second method evolves simultaneously the whole classifier (embedded approach). The performance of these two algorithms was compared to that of two manual and two automatic neural network optimisation techniques on thirteen well-known pattern recognition benchmarks. The experimental results revealed the specific strengths and weaknesses of the six algorithms. Overall, the evolutionary embedded method obtained good results in terms of classification accuracy and compactness of the solutions. The tests indicated that the outcome of the feature selection task has a major impact on the accuracy and compactness of the solutions. Evolutionary algorithms perform best on feature spaces of small and medium size, and were the most effective at rejecting redundant features. Classical filter-based algorithms based on feature correlation are preferable on undersampled data sets. Correlation- and saliency-based selection was the most effective method in the presence of a large number of irrelevant features. The applicability and performance of the wrapper algorithm was severely limited by the computational costs of the approach.