Schütze H, Hull D, Pedersen J. 1995. A comparison of classifiers and document representations for the routing problem. In Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press: New York, NY, USA; 229–237.
Serrano Cinca C. 1996. Self organizing neural networks for financial diagnosis. Decision Support Systems 17(3): 227–238.
Serrano Cinca C. 1998a. Self-organizing maps for initial data analysis: let financial data speak for themselves. In Visual Intelligence in Finance Using Self-Organizing Maps. Deboeck G, Kohonen T (eds). Springer Verlag.
Serrano Cinca C. 1998b. From financial information to strategic groups—a self organizing neural network approach. Journal of Forecasting 17: 415–428.
Sexton RS, Gupta JND. 2000. Comparative evaluation of genetic algorithm and backpropagation for training neural networks. Information Sciences 129: 45–49.
Sexton RS, Sikander NA. 2001. Data mining using a genetic algorithm-trained neural network. International Journal of Intelligent Systems in Accounting, Finance and Management 10: 201–210.
Sexton RS, Dorsey RE, Johnson JD. 1998. Toward a global optimum for neural networks: a comparison of the genetic algorithm and backpropagation. Decision Support Systems 22(2): 171–186.
Shimodaira H. 1996. A new genetic algorithm using large mutation rates and population-elitist selection (GALME). In Proceedings of the 8th International Conference on Tools with Artificial Intelligence (ICTAI ’96); 25–32.
Siegel S, Castellan Jr NJ. 1988. Nonparametric Statistics for the Behavioral Sciences, 2nd edition. McGrawHill International Editions.
Tuson A, Ross P. 1998. Adapting operator settings in genetic algorithms. Evolutionary Computation 6(2): 161– 184.
Upadhyaya BR, Eryurek E. 1992. Application of neural network for sensory validation and plant monitoring. Neural Technology 97: 170–176.
Vafaie H, DeJong K. 1998. Feature space transformation using genetic algorithms. IEEE Intelligent Systems 13(2): 57–65.
Yao X. 1999. Evolving artificial neural networks. Proceedings of the IEEE 87(9): 1423–1447.
Yao X, Liu Y. 1997. A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks 8(3): 694–713.
Zavgren C. 1985. Assessing the vulnerability to failure of American industrial firms: a logistics analysis. Journal of Business Finance and Accounting 12(1): 19–45.
Zupan B, Bohanec M, Demsar J, Bratko I. 1998. Feature transformation by function decomposition. IEEE Intelligent Systems 13(2): 38–43.
Zupan B, Demsar J, Kattan MW, Ohori M, Graefen M, Bohanec M, Beck JR. 2001. Orange and decisions-athand: bridging predictive data mining and decision support. In IDDM-2001: ECML/PKDD-2001 Workshop Integrating Aspects of Data Mining, Decision Support and Meta-Learning, Freiburg, Giraud-Carrier C, Lavrac N, Moyle S, Kavsek B (eds); 151–162.
[1] The self-organizing map was introduced by Kohonen in the early 1980s and is an unsupervised learning technique that creates a two-dimensional topological map from n-dimensional input data. A topological map is a mapping that preserves neighbourhood relations. Similar input vectors have close positions on the map.
[2] The permutation problem occurs because two ANNs with different architectures can have the same performance. In other words, even though the two genetic representations of the networks are different, the networks have the same functionality. The permutation problem makes a crossover operator very inefficient and ineffective, since with this operator (permutation of hidden nodes) functionally equivalent networks are obtained (Yao, 1999: 1426).
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