H. N. Bensusan, Automatic Bias Learning: An Inquiry into the Inductive Basis of Induction. Ph.D. Dissertation, School of Computing and Cognitive Sciences, University of Sussex, Sussex, 1998.
H. N. Bensusan and I. Kuscu, “Constructive induction using genetic programming,” in Proc. Int. Conf. Machine Learning, Evolutionary Computing and Machine Learning Workshop, T. Fogarty and G. Venturini (eds.), Bari, Italy, 1996.
B. Bhanu and K. Krawiec, “Coevolutionary construction of features for transformation of representation in machine learning,” in Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference (GECCO 2002), A. M. Barry (ed.), AAAI Press: New York, 2002, pp. 249–254.
C. L. Blake and C. J. Merz, “UCI Repository of machine learning databases,” [http://www.ics.uci.edu/~mlearn/MLRepository.html], University of California: Irvine, CA, 1998.
M. Brameier and W. Banzhaf, “Evolving teams of predictors with linear genetic programming,” Genetic Programming and Evolvable Machines vol. 2, pp. 381–407, 2001.
C. E. Brodley and A. Pohoreckyj Danyluk (eds.), Proc. Int. Conf. Machine Learning, Morgan Kaufmann: San Francisco 2001.
M. Dash and H. Liu, “Feature selection for classification,” Intelligent Data Analysis vol. 1, no. 3, pp. 131–156, 1997.
K. A. De Jong, An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Doctoral dissertation, University of Michigan, Ann Arbor, 1975.
K. A. De Jong, W. M. Spears, and D. F. Gordon, “Using genetic algorithms for concept learning,” Machine Learning vol. 13, pp. 161–188, 1993.
D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley: Reading, 1989.
J. J. Grefenstette, “Lamarckian learning in multi-agent environments,” in Proc. Fourth Intl. Conf. of Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1991, pp. 303–310.
J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press: Ann Arbor, 1975.
I. Imam and H. Vafaie, “An empirical comparison between global and greedy-like search for feature selection,” in Proceedings of the Florida AI Research Symposium (FLAIRS-94), Pensacola Beach, FL, 1994, pp. 66–70.
J. K. Kishore, L. M. Patnaik, V. Mani, and V. K. Agrawal, “Application of genetic programming for multicategory pattern classification,” IEEE Trans. Evolutionary Comp. vol. 4, no. 3, pp. 242–258, 2000.
M. Komosinski and K. Krawiec, “Evolutionary weighting of image features for diagnosing of CNS tumors,” Artif. Intell. in Medicine vol. 19, no. 1, pp. 25–38, 2000.
R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artif. Intell. Journal vol. 1–2, pp. 273–324, 1997.
R. Kohavi, D. Sommerfeld, and J. Dougherty, “Data mining using MLC++: Amachine learning library in C++,” in Proc. of the Eight International Conference on Tools with Artificial Intelligence (ICTAI'96), IEEE Computer Society, 1996, pp. 234–245.
J. R. Koza, Genetic Programming—2, MIT Press: Cambridge, 1994.
K. Krawiec, “Pairwise comparison of hypotheses in evolutionary learning,” in Proc. Int. Conf. Machine Learning, C. E. Brodley and A. Pohoreckyj Danyluk (eds.), Morgan Kaufmann: San Francisco, 2001, pp. 266–273.
K. Krawiec, “Genetic programming with local improvement for visual learning from examples,” in Computer Analysis of Images and Patterns (LNCS 2124), W. Skarbek, (ed.), Springer: Berlin, 2001, pp. 209–216.
K. Krawiec, “On the use of pairwise comparison of hypotheses in evolutionary learning applied to learning from visual examples,” in Machine Learning and Data Mining in Pattern Recognition (LNAI 2123), P. Perner (ed.), Springer: Berlin, 2001, pp. 307–321.
P. Langley, Elements of Machine Learning, Morgan Kaufmann: San Francisco, 1996.
T.-S. Lim, W.-Y. Loh, and Y.-S. Shih, “Acomparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms,” Machine Learning, vol. 40, pp. 203–228, 2000.
S. Luke, “ECJ 7: An EC and GP system in Java,” http://www.cs.umd.edu/projects/plus/ec/ecj/, 2001.
C. J. Matheus and L. A. Rendell, “Constructive induction on decision trees,” in Proc. of the Eleventh International Joint Conference on Artificial Intelligence, N. S. Sridharan (ed.), Detroit, MI, USA, August 1989, Morgan Kaufmann 1989, pp. 645–650.
C. J. Matheus, “Aconstructive induction framework,” in Proc. of the Sixth International Workshop on Machine Learning, Ithaca: New York, 1989, pp. 474–475.
G. Mayraz and G. Hinton, “Recognizing hand-written digits using hierarchical products of experts,” Adv. NIPS 13, MIT Press: Cambridge, MA, 2001, pp. 953–959.
R. S. Michalski, “Atheory and methodology of inductive learning,” Artificial Intelligence 20, pp. 111–161, 1983.
T. M. Mitchell, An Introduction to Genetic Algorithms, MIT Press: Cambridge, MA, 1996.
T. M. Mitchell, Machine Learning, McGraw-Hill: New York, 1997.
J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann: San Mateo, CA, 1992.
M. L. Raymer, W. F. Punch, E. D. Goodman, L. A. Kuhn, and A. K. Jain, “Dimensionality reduction using genetic algorithm,” IEEE Trans. on Evolutionary Computation, vol. 4, no. 2, pp. 164–171, 2000.
M. L. Raymer, W. F. Punch, E. D. Goodman, and L. A. Kuhn, “Genetic programming for improved data mining—application to the biochemistry of protein interactions,” in Genetic Programming 1996: Proceedings, J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, (eds.), MIT Press: Cambridge, MA, 1996, pp. 275–381.
S. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J. Cheng, K. De Jong, S. Dzeroski, R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R. S. Michalski, T. Mitchell, P. Pachowicz, B. Roger, H. Vafaie, W. Van de Velde, W. Wenzel, J. Wnek, and J. Zhang, “The MONK's problems: Aperformance comparison of different learning algorithms,” Technical Report CMU-CS-91-197, Carnegie Mellon University: Pittsburgh, PA, 1991.
I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann: San Francisco, CA, 1999.
J. Yang and V. Honavar, “Feature subset selection using a genetic algorithm,” IEEE Intelligent Systems (Special Issue on Feature Transformation and Subset Selection), vol. 13, pp. 44–49, 1998.