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
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
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
Neural networks in designing fuzzy systems for real world applications
Fuzzy Sets and Systems
Knowledge-based artificial neural networks
Artificial Intelligence
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
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
Statistical Control of RBF-like Networks for Classification
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
Strongly typed genetic programming
Evolutionary Computation
Are artificial neural networks black boxes?
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
IEEE Transactions on Neural Networks
Knowledge-internalization process for neural-networks practitioners
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.