Unsupervised Linkage Learner Based on Local Optimums

  • Hamid Parvin
  • Sajad Parvin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7329)

Abstract

Genetic Algorithms (GAs) are categorized as search heuristics and have been broadly applied to optimization problems. These algorithms have been used for solving problems in many applications, but it has been shown that simple GA is not able to effectively solve complex real world problems. For proper solving of such problems, knowing the relationships between decision variables which is referred to as linkage learning is necessary. In this paper a linkage learning approach is proposed that utilizes the special features of the decomposable problems to solve them. The proposed approach is called Linkage Learner based on Local Optimums and Clustering (LLLC). The LLLC algorithm is capable of identifying the groups of variables which are related to each other (known as linkage groups), no matter if these groups are overlapped or different in size. The proposed algorithm, unlike other linkage learning techniques, is not done along with optimization algorithm; but it is done in a whole separated phase from optimization search. After finding linkage group information by LLLC, an optimization search can use this information to solve the problem. LLLC is tested on some benchmarked decomposable functions. The results show that the algorithm is an efficient alternative to other linkage learning techniques.

Keywords

Linkage Learning Optimization Problems Decomposable Functions 

References

  1. 1.
    Audebert, P., Hapiot, P.: Effect of powder deposition. J. Electroanal. Chem. 361, 177 (1993)CrossRefGoogle Scholar
  2. 2.
    Newman, J.: Electrochemical Systems, 2nd edn. Prentice-Hall, Englewood Cliffs (1991)Google Scholar
  3. 3.
    Hillman, A.R.: Electrochemical Science and Technology of Polymers, vol. 1, ch. 5. Elsevier, Amsterdam (1987)Google Scholar
  4. 4.
    Miller, B.: Geelong, Vic. J. Electroanal. Chem. 168, 19–24 (1984)Google Scholar
  5. 5.
    Jones: personal communication (1992)Google Scholar
  6. 6.
    Pelikan, M., Goldberg, D.E.: Escaping hierarchical traps with competent genetic algorithms. In: Genetic and Evolutionary Computation Conference, GECCO, pp. 511–518 (2001)Google Scholar
  7. 7.
    Pelikan, M., Goldberg, D.E.: A hierarchy machine: Learning to optimize from nature and humans. Complexity 8(5) (2003)Google Scholar
  8. 8.
    Pelikan, M.: Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms. Springer (2005)Google Scholar
  9. 9.
    Strehl, A., Ghosh, J.: Cluster Ensembles — A Knowledge Reuse Framework for Combining Multiple Partitions. Journal of Machine Learning Research 3, 583–617 (2002)MathSciNetGoogle Scholar
  10. 10.
    Stuart, R., Peter, N.: Artificial Intelligence: A Modern Approach, 2nd edn., pp. 111–114. Prentice Hall (2003)Google Scholar
  11. 11.
    Parvin, H., Minaei-Bidgoli, B.: Linkage Learning Based on Local Optimums Clustering of Building Blocks. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 163–172. Springer, Heidelberg (2011) ISSN: 0302-9743CrossRefGoogle Scholar
  12. 12.
    Parvin, H., Minaei-Bidgoli, B., Helmi, B.H.: Linkage Learning Based on Differences in Local Optimums of Building Blocks with One Optima. In: International Conference on Computational Intelligence in Security for Information Systems. LNCS, pp. 286–293. Springer, Heidelberg (2011) ISSN: 0302-9743Google Scholar
  13. 13.
    Minaei-Bidgoli, B., Parvin, H., Alinejad-Rokny, H., Alizadeh, H., Punch, W.F.: Effects of resampling method and adaptation on clustering ensemble efficacy, Online (2011)Google Scholar
  14. 14.
    Parvin, H., Minaei-Bidgoli, B.: Linkage Learning Based on Local Optima. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 163–172. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Parvin, H., Helmi, H., Minaei-Bidgoli, B., Alinejad-Rokny, H., Shirgahi, H.: Linkage Learning Based on Differences in Local Optimums of Building Blocks with One Optima. International Journal of the Physical Sciences 6(14), 3419–3425 (2011)Google Scholar
  16. 16.
    Parvin, H., Minaei-Bidgoli, B., Alizadeh, H.: A New Clustering Algorithm with the Convergence Proof. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part I. LNCS, vol. 6881, pp. 21–31. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Parvin, H., Minaei, B., Alizadeh, H., Beigi, A.: A Novel Classifier Ensemble Method Based on Class Weightening in Huge Dataset. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part II. LNCS, vol. 6676, pp. 144–150. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Parvin, H., Minaei-Bidgoli, B., Alizadeh, H.: Detection of Cancer Patients Using an Innovative Method for Learning at Imbalanced Datasets. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 376–381. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Parvin, H., Minaei-Bidgoli, B., Ghaffarian, H.: An Innovative Feature Selection Using Fuzzy Entropy. In: Liu, D. (ed.) ISNN 2011, Part III. LNCS, vol. 6677, pp. 576–585. Springer, Heidelberg (2011)Google Scholar
  20. 20.
    Parvin, H., Minaei, B., Parvin, S.: A Metric to Evaluate a Cluster by Eliminating Effect of Complement Cluster. In: Bach, J., Edelkamp, S. (eds.) KI 2011. LNCS, vol. 7006, pp. 246–254. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Parvin, H., Minaei-Bidgoli, B., Ghatei, S., Alinejad-Rokny, H.: An Innovative Combination of Particle Swarm Optimization, Learning Automaton and Great Deluge Algorithms for Dynamic Environments. International Journal of the Physical Sciences 6(22), 5121–5127 (2011)Google Scholar
  22. 22.
    Parvin, H., Minaei, B., Karshenas, H., Beigi, A.: A New N-gram Feature Extraction-Selection Method for Malicious Code. In: Dobnikar, A., Lotrič, U., Šter, B. (eds.) ICANNGA 2011, Part II. LNCS, vol. 6594, pp. 98–107. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Qodmanan, H.R., Nasiri, M., Minaei-Bidgoli, B.: Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Systems with Applications 38(1), 288–298 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hamid Parvin
    • 1
  • Sajad Parvin
    • 1
  1. 1.Nourabad Mamasani BranchIslamic Azad UniversityNourabad MamasaniIran

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