Fractally configured neural networks
Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Alternative Neural Network Training Methods
IEEE Expert: Intelligent Systems and Their Applications
Genetic Synthesis of Modular Neural Networks
Proceedings of the 5th International Conference on Genetic Algorithms
Fast Reinforcement Learning through Eugenic Neuro-Evolution
Fast Reinforcement Learning through Eugenic Neuro-Evolution
A constructive algorithm for binary neural networks: the oil-spot algorithm
IEEE Transactions on Neural Networks
ALEC: An Adaptive Learning Framework for Optimizing Artificial Neural Networks
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
MARS: Still an Alien Planet in Soft Computing?
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Is Neural Network a Reliable Forecaster on Earth? A MARS Query!
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Intelligent systems: architectures and perspectives
Recent advances in intelligent paradigms and applications
Application of adaptive neuro-fuzzy controller for SRM
Advances in Engineering Software
Evolutionary Bi-objective Learning with Lowest Complexity in Neural Networks: Empirical Comparisons
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
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Selection of the topology of a network and correct parameters for the learning algorithm is a tedious task for designing an optimal Artificial Neural Network (ANN), which is smaller, faster and with a better generalization performance. Genetic algorithm (GA) is an adaptive search technique based on the principles and mechanisms of natural selection and survival of the fittest from natural evolution. Simulated annealing (SA) is a global optimization algorithm that can process cost functions possessing quite arbitrary degrees of nonlinearities, discontinuities and stochasticity but statistically assuring a optimal solution. In this paper we explain how a hybrid algorithm integrating the desirable aspects of GA and SA can be applied for the optimal design of an ANN. This paper is more concerned with the understanding of current theoretical developments of Evolutionary Artificial Neural Networks (EANNs) using GAs and other heuristic procedures and how the proposed hybrid and other heuristic procedures can be combined to produce an optimal ANN.