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Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
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Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
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Neural Networks: A Comprehensive Foundation
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Towards the Genetic Synthesisof Neural Networks
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Programming of Minimal Neural Nets Using Occam's Razor
Proceedings of the 5th International Conference on Genetic Algorithms
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Evolving neural networks through augmenting topologies
Evolutionary Computation
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Proceedings of the Second European Workshop on Genetic Programming
Evolutionary Subsethood Product Fuzzy Neural Network
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Discovering efficient learning rules for feedforward neural networks using genetic programming
Recent advances in intelligent paradigms and applications
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Neural Computation
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Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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Applied Soft Computing
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Artificial Life
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PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
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Evolutionary computation is a class of global search techniquesbased on the learning process of a population of potential solutions to agiven problem, that has been successfully applied to a variety of problems.In this paper a new approach to the construction of neural networks based onevolutionary computation is presented. A linear chromosome combined to agraph representation of the network are used by genetic operators, whichallow the evolution of the architecture and the weights simultaneouslywithout the need of local weight optimization. This paper describes theapproach, the operators and reports results of the application of thistechnique to several binary classification problems.