Top > JACIII > Adaptation and Self-Adaptation Mechanisms in Genetic Network P ...

single-jc.php

JACIII Vol.11 No.3 pp. 343-353
doi: 10.20965/jaciii.2007.p0343
(2007)

Paper:

Views over last 60 days: 426

Adaptation and Self-Adaptation Mechanisms in Genetic Network Programming for Mining Association Rules

Karla Taboada, Eloy Gonzales, Kaoru Shimada,
Shingo Mabu, Kotaro Hirasawa, and Jinglu Hu

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

Received:
November 13, 2006
Accepted:
January 5, 2007
Published:
March 20, 2007
Creative Commons License
Keywords:
evolutionary computation, genetic network programming (GNP), data mining, association rules, adaptation/self-adaptation
Abstract
In this paper we propose a method of association rule mining using Genetic Network Programming (GNP) with adaptive and self-adaptive mechanisms of genetic operators in order to improve the performance of association rule extraction systems. GNP is one of the evolutionary methods, whose directed graphs are evolved to find a solution as individuals. Adaptation behavior in GNP is related to adjust the setting of control parameters such as the proportion of crossover and mutation. The aim is not only to find suitable adjustments but to do it efficiently. Regarding to self-adaptation, the algorithm controls the setting of these parameters themselves – embedding them into an individual’s genome and evolving them, and it usually changes the structure of the evolution which is typically static. Specifically, self-adaptation of crossover and mutation operators in GNP aiming to change the rate of them by evolution is studied in this paper. Our method based on GNP can measure the significance of the association via the chi-squared test and obtain a sufficient number of important association rules. Extracted association rules are stored in a pool all together through generations and reflected in three genetic operators as acquired information.
Cite this article as:
K. Taboada, E. Gonzales, K. Shimada, S. Mabu, K. Hirasawa, and J. Hu, “Adaptation and Self-Adaptation Mechanisms in Genetic Network Programming for Mining Association Rules,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.3, pp. 343-353, 2007.
Data files:
References
  1. [1] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” In Proc. of the 20th VLDB Conf., pp. 487-499, 1994.
  2. [2] S. Brin, R. Motwani, and C. Silverstein, “Beyond market baskets: generalizing association rules to correlations,” in Proc. of the 1997 ACM SIGMOD Conf., pp. 265-276, 1997.
  3. [3] T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, “A study of Evolutionary Multiagent Models Based on Symbiosis,” IEEE Trans. on Syst., Man and Cybernetics –Part B–, Vol.36, No.1, pp. 179-193, 2006.
  4. [4] S. Mabu, K. Hirasawa, and J. Hu, “A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning,” Evolutionary Computation, MIT press (to appear).
  5. [5] K. Hirasawa, M. Okubo, H. Katagiri, J. Hu, and J. Murata, “Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP),” in Proc. of Congress of Evolutionary Computation, pp. 1276-1282, 2001.
  6. [6] K. Shimada, K. Hirasawa, and J. Hu, “Genetic Network Programming with Acquisition Mechanisms of Association Rules,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.1, pp. 102-111, 2006.
  7. [7] K. Shimada, K. Hirasawa, and J. Hu, “Class Association Rule Mining with Chi-Squared Test Using Genetic Network Programming,” in Proc. of IEEE SMC 2006, pp. 5338-5344.
  8. [8] P. J. Angeline, “Adaptive and self-adaptive evolutionary computations,” in M. Palaniswami and Y. Attikiouzel (Eds.), Computational Intelligence: A Dynamic Systems Perspective, IEEE Press, pp. 152-163, 1995.
  9. [9] J. H. Holland, “Adaptation in Natural and Artificial Systems,” The University of Michigan Press, Ann Arbor, 1975.
  10. [10] S. Meyer-Nieberg and H.-G. Beyer, “Self-Adaptation in Evolutionary Algorithms,” Department for Computer Science, University of Bundeswehr Munchen, Neubiberg, Germany, pp. 1-29.
  11. [11] D. B. Fogel, “Evolutionary Computation: Toward a New Philosophy of Machine Intelligence,” IEEE Press, New York, NY, 1995.
  12. [12] A. V. Sebald, “On exploiting the global information generated by evolutionary programs,” in Proc. of the First Annual Conference on Evolutionary Programming, Evolutionary Programming Society, San Diego, CA, 1992.
  13. [13] T. Dunning, “Directional Mutation for Faster Convergence,” Computing Research Laboratory, New Mexico State University, September 25, 1995.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024