A future development will be the analysis of the different parameters that intervene in the correct functioning of the algorithm, depending on the type of problem solved by the ANN. The ANN should be treated not as a “black box,” but as a “grey box,” in which, for instance, the activation function of the ANN is known, being incorporated as one of the mathematical operators of GP, and analysing which rules are extracted by this operator.
In order to accelerate the rule-extraction process, a network may be used with several computers so that the search is conducted in a distributed and concurrent manner, exchanging rules (sub-trees) between each.
References
Andrews, R., Cable, R., Diederich, J., Geva, S., Golea, M., Hayward, R., Ho-Stuart, C., &
Tickle A.B. (1996). An evaluation and comparison of techniques for extracting and refining rules from artificial neural networks. Queensland University of Technology, Neurocomputing Research Centre. QUT NRC Technical report.
Andrews, R., Diederich, J., & Tickle, A. (1995). A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge Based Systems, 8, 373-389.
Andrews, R., & Geva, S. (1994). Rule extraction from a constrained error backpropagation MLP. Proceedings of the Australian Conference on Neural Networks, Brisbane, Queensland (pp. 9-12).
Benítez, J.M., Castro, J.L., & Requena, I. (1997). Are artificial neural networks black boxes? IEEE Transactions on Neural Networks, 8(5), 1156-1164.
Blake, C.L., & Mertz ,C.J. (1998). UCI repository of machine learning databases.
University of California, Department of Information and Computer Science. Re-trieved from www-old.ics.uci.edu/pub/machine-learning-databases
Bonarini, A. (1996). Evolutionary learning of fuzzy rules: Competition and cooperation.
In W. Pedrycz (Ed.), Fuzzy modelling: Paradigms and practice. Norwell, MA:
Kluwer Academic Press.
Browne, C., Düntsch, I., & Gediga, G. (1998). IRIS revisited: A comparison of discriminant and enhanced rough set data analysis. In Rough sets in knowledge discovery (vol.
2, pp. 345-368). Heidelberg: Physica Verlag.
Buckley, J.J., Hayashi, Y., & Czogala, E. (1993). On the equivalence of neural nets and fuzzy expert systems. Fuzzy Sets Systems, 53, 29-134.
Chalup, S., Hayward, R., & Diedrich, J. (1998). Rule extraction from artificial neural networks trained on elementary number classification task. Queensland Univer-sity of Technology, Neurocomputing Research Centre. QUT NRC Technical report.
Cramer, N.L. (1985). A representation for the adaptive generation of simple sequential programs. Grefenstette: Proceedings of First International Conference on Ge-netic Algorithms.
Craven, M.W. (1996). Extracting comprehensible models from trained neural networks.
Ph.D. thesis, University of Wisconsin, Madison.
Craven, M.W., & Shavlik, J.W. (1996). Extracting tree-structured representations of trained networks. Advances in neural information processing systems (vol. 8).
Cambridge, MA: MIT Press.
Duch, W., Adamczak, R., & Grbczewski, K. (2001). A new methodology of extraction, optimisation and application of crisp and fuzzy logical rules. IEEE Transactions on Neural Networks, 12, 277-306.
Engelbrecht, A.P., Rouwhorst, S.E., & Schoeman, L. (2001). A building block approach to genetic programming for rule discovery. In R. Abbass & C. Newton (Eds.), Data mining: A heuristic approach. Hershey, PA: Idea Group Publishing.
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining. AAAI/MIT Press.
Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, 179-188.
Friedberg, R.M. (1958). A learning machine: Part I. IBM Journal of Research and Development, 2(1), 2-13.
Friedberg, R.M., Dunham, B., & North, J.H. (1959). A learning machine: Part II. IBM Journal of Research and Development, 3(3), 282-287.
Fujiki, C. (1987). Using the genetic algorithm to generate lisp source code to solve the prisoner’s dilemma. International Conf on Gas, 236-240.
Halgamuge, S.K., & Glesner, M. (1994) Neural networks in designing fuzzy systems for real world applications. Fuzzy Sets and Systems, 65, 1-12.
Jagielska, I., Matthews, C., & Whitfort, T. (1996). The application of neural networks, fuzzy logic, genetic algorithms and rough sets to automated knowledge acquisi-tion. 4th Int. Conf. on Soft Computing, IIZUKA’96, Japan (vol. 2, pp. 565-569).
Jang, J., & Sun, C. (1992). Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Transactions on Neural Networks, 4, 156-158.
Jankowski, N., & Kadirkamanathan, V. (1997). Statistical control of RBF-like networks for classification. 7th International Conference on Artificial Neural Networks, Lausanne, Switzerland (pp. 385-390).
Kasabov, N. (1996). Foundations of neural networks, fuzzy systems and knowledge engineering. Cambridge, MA: MIT Press.
Keedwell, E., Narayanan, A., & Savic, D. (2000). Creating rules from trained neural networks using genetic algorithms. International Journal of Computers, Systems and Signals (IJCSS), 1(1), 30-42.
Koza, J. (1992). Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press.
Koza, J. (1994). Genetic programming II: Automatic discovery of reusable programs (complex adaptive systems). Cambridge, MA: The MIT Press.
Koza, J., Bennett, F.H., Andre, D., & Keane, M.A. (1999). Genetic programming III:
Darwinian invention and problem solving. Morgan Kauffman.
Mackey, M., & Glass, L. (1977). Oscillation and chaos in physiological control systems.
Science, 197, 287.
Martínez, A., & Goddard, J. (2001). Definición de una red neuronal para clasificación por medio de un programa evolutivo. Revista Mexicana de Ingeniería Biomédica, 22(1), 4-11.
Montana, D.J. (1995). Strongly typed genetic programming. In Evolutionary computa-tion (pp. 199-200). Cambridge, MA: MIT Press.
Nauck, D., Nauck, U., & Kruse, R. (1996). Generating classification rules with the neuro-fuzzy system NEFCLASS. Proceedings of Biennal Conference of the North American Fuzzy Information Processing Society (NAFIPS’96), Berkeley, CA.
Pop, E., Hayward, R., & Diederich, J. (1994). RULENEG: Extracting rules from a trained ANN by stepwise negation. Queensland University of Technology, Neurocomputing Research Centre. QUT NRC Technical report.
Rabuñal, J.R. (1999). Entrenamiento de Redes de Neuronas Artificiales mediante Algoritmos Genéticos. Graduate thesis, Facultad de Informática, Universidade da Coruña.
Rabuñal, J.R., Dorado, J., Pazos, A., Pereira, J., & Rivero, D. (2004). A new approach to the extraction of ANN rules and to their generalization capacity through GP. Neural Computation, 16(7), 1483-1524.
Shang, N., & Breiman, L. (1996). Distribution based trees are more accurate. International Conference on Neural Information Processing (vol. 1, pp. 133-138). Hong Kong.
Ster, B., & Dobnikar, A. (1996). Neural networks in medical diagnosis: Comparaison with other methods. Proceedings of the International Conference on Engineering Applications of Neural Networks, EANN’96 (pp. 427-430).
Thrun, S. (1995). Extracting rules from networks with distributed representations. In G.
Tesauro, D. Touretzky, & T. Leens (Eds.), Advances in neural information processing systems (NIPS) (vol. 7). Cambridge, MA: MIT Press.
Tickle, A.B., Andrews, R., Golea, M., & Diederich, J. (1998). The truth will come to light:
Directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Transaction on Neural Networks, 9(6), 1057-1068.
Tickle, A.B., Orlowski, M., & Diedrich J. (1996). DEDEC: A methodology for extracting rules from trained artificial neural networks. Queensland University of Technol-ogy, Neurocomputing Research Centre. QUT NRC Technical report.
Towell, G., & Shavlik, J.W. (1994). Knowledge-based artificial neural networks. Artificial Intelligence,70, 119-165.
Visser, U., Tickle, A., Hayward, R., & Andrews, R. (1996). Rule-extraction from trained neural networks: Different techniques for the determination of herbicides for the plant protection advisory system PRO_PLANT. Proceedings of the rule extrac-tion from trained artificial neural networks workshop, Brighton, UK (pp. 133-139).
Weiss, S.M., & Kulikowski, C.A. (1990). Computer systems that learn. San Mateo, CA:
Morgan Kauffman.
Wong, M.L., & Leung, K.S. (2000). Data mining using grammar based genetic program-ming and applications. Kluwer Academic Publishers.