Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Discovering several robot behaviors through speciation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Genetic drift in genetic algorithm selection schemes
IEEE Transactions on Evolutionary Computation
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
Systematically incorporating domain-specific knowledge into evolutionary speciated checkers players
IEEE Transactions on Evolutionary Computation
Evolutionary neural networks for practical applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In a genetic algorithm, the search process maintains multiple solutions and their interactions are important to accelerate the evolution. If the pool of solutions is dominated by the single fittest individual in the early generation, there is a risk of premature convergence losing exploration capability. It is necessary to consider not only the fitness of solutions but also the similarity to other individuals. This speciation idea is beneficial to several application domains with evolutionary computation but it requires objective distance measures to calculate the similarity of individuals. It raises a challenging research issue to measure the distance between two evolutionary neural networks (ENN). In this paper, we surveyed several distance measures proposed for ENN and compared their performance for pattern classification problems with two different genetic representations (matrix-based and topology growing (NEAT) approaches). Although there was no dominant distance measure for the pattern classification problems, it showed that the behavioral distance measures outperformed the architectural one for matrix-based representation and. For NEAT, NeuroEdit showed better accuracy against compatibility distance measure.