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Integration of supervised ART-based neural networks with a hybrid genetic algorithm
In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process of the proposed EANN models is guided by a knowledge base established by ART with respect to the training data samples. The EANN models explore the search space for “coarse” solutions, and such solutions are then refined using the local search process of the HGA. The performances of the proposed EANN models are evaluated and compared with those from other classifiers using more than ten benchmark data sets. The applicability of the EANN models to a real medical classification task is also demonstrated. The results from the experimental studies demonstrate the effectiveness and usefulness of the proposed EANN models in undertaking pattern classification problems.
History
Journal
Soft computingVolume
15Issue
2Pagination
205 - 219Publisher
SpringerLocation
Heidelberg, GermanyISSN
1432-7643eISSN
1433-7479Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2010, Springer-VerlagUsage metrics
Keywords
dynamic decay adjustment algorithmevolutionary artificial neural networkfuzzy ARTMAPhybrid genetic algorithmpattern classificationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Interdisciplinary ApplicationsComputer ScienceMEMETIC ALGORITHMEVOLUTIONARY OPTIMIZATIONPATTERN-RECOGNITIONFEATURE-SELECTIONDESIGNARCHITECTUREPARAMETERSCROSSOVERONLINEArtificial Intelligence and Image Processing
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