Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Neural network design
Computer Vision and Fuzzy-Neural Systems
Computer Vision and Fuzzy-Neural Systems
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
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
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Circle detection on images using genetic algorithms
Pattern Recognition Letters
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Classification Techniques of Neural Networks Using Improved Genetic Algorithms
WGEC '08 Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing
Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab
Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab
Evolving hardware using a new evolutionary algorithm based on evolution of a species
International Journal of Bio-Inspired Computation
Multi-objective genetic algorithm evaluation in feature selection
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
How many neurons?: a genetic programming answer
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Multi-objective genetic programming for visual analytics
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
A study on the mutation rates of a genetic algorithm interacting with a sandpile
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Bat algorithm for multi-objective optimisation
International Journal of Bio-Inspired Computation
Genetic fuzzy relational neural network for infant cry classification
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Control of a service robot using the mexican sign language
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Genetic algorithm and pure random search for exosensor distribution optimisation
International Journal of Bio-Inspired Computation
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This paper discusses the generation of neural networks that are obtained from the evolution of individual's population in a genetic algorithm. For achieving this, the population of individuals for the genetic algorithm is formed of structural elements which constitute the neural networks. These elements include the number of layers, neurons per layer, transfer functions and the connections between neurons in the network, among others. These individuals as can be seen a structure which has the ability to evolve rather than a standard genotype. Furthermore, the size of the individuals is not defined and depends mainly on the neural network which in turn depends on the problem to be solved. This structure considered as an evolutionary entity, is able to evolve until convergence towards a suitable structure is achieved. The fitness function is specified with the features of the problem to be solved by the neural network. This algorithm has been tested successfully in solving classification problems, as in the case of alpha-numerical character recognition, and has been compared against a neural network obtained by conventional means. Better results were obtained with the neural network generated by using genetic programming of this type of evolutionary entities.