Using the genetic algorithm to generate LISP source code to solve the prisoner's dilemma
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
On the equivalence of neural nets and fuzzy expert systems
Fuzzy Sets and Systems
Knowledge-based artificial neural networks
Artificial Intelligence
Data Mining Using Grammar-Based Genetic Programming and Applications
Data Mining Using Grammar-Based Genetic Programming and Applications
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
IBM Journal of Research and Development
Are artificial neural networks black boxes?
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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Different techniques for extracting Artificial Neural Networks (ANN) rules have been used up to the present time, but most of them have focused on certain types of networks and their training. However, there are practically no methods which deal with ANN rule-discovery as systems that are independent from their architecture, training, and internal distribution of weights, connections, and activation functions. This paper proposes a method based on Genetic Programming (GP) with the purpose of achieving the generalization capacity characteristic of ANNs, by means of symbolic rules which can be understood by human beings.