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.
Chapter PDF
Similar content being viewed by others
References
Audebert, P., Hapiot, P.: Effect of powder deposition. J. Electroanal. Chem. 361, 177 (1993)
Newman, J.: Electrochemical Systems, 2nd edn. Prentice-Hall, Englewood Cliffs (1991)
Hillman, A.R.: Electrochemical Science and Technology of Polymers, vol. 1, ch. 5. Elsevier, Amsterdam (1987)
Miller, B.: Geelong, Vic. J. Electroanal. Chem. 168, 19–24 (1984)
Jones: personal communication (1992)
Pelikan, M., Goldberg, D.E.: Escaping hierarchical traps with competent genetic algorithms. In: Genetic and Evolutionary Computation Conference, GECCO, pp. 511–518 (2001)
Pelikan, M., Goldberg, D.E.: A hierarchy machine: Learning to optimize from nature and humans. Complexity 8(5) (2003)
Pelikan, M.: Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms. Springer (2005)
Strehl, A., Ghosh, J.: Cluster Ensembles — A Knowledge Reuse Framework for Combining Multiple Partitions. Journal of Machine Learning Research 3, 583–617 (2002)
Stuart, R., Peter, N.: Artificial Intelligence: A Modern Approach, 2nd edn., pp. 111–114. Prentice Hall (2003)
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: 0302-9743
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: 0302-9743
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Parvin, H., Parvin, S. (2012). Unsupervised Linkage Learner Based on Local Optimums. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera López, J.A., Boyer, K.L. (eds) Pattern Recognition. MCPR 2012. Lecture Notes in Computer Science, vol 7329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31149-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-31149-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31148-2
Online ISBN: 978-3-642-31149-9
eBook Packages: Computer ScienceComputer Science (R0)