A data mining based genetic algorithm

Genetic algorithms (GAs) are considered as a global search approach for optimization problems. Through the proper evaluation strategy, the best "chromosome" can be found from the numerous genetic combinations. Although the GA operations do provide the opportunity to find the optimum solution, they may fail in some cases, especially when the length of a chromosome is very long. In this paper, a data mining-based GA is presented to efficiently improve the traditional GA (TGA). By analyzing support and confidence parameters, the important genes, called DNA, can be obtained. By adopting DNA extraction, it is possible that TGA will avoid stranding on a local optimum solution. Furthermore, the new GA operation, DNA implantation, was developed for providing potentially high quality genetic combinations to improve the performance of TGA. Experimental results in the area of digital watermarking show that our data mining-based GA successfully reduces the number of evolutionary iterations needed to find a solution

[1]  Frank Y. Shih,et al.  Decomposition of binary morphological structuring elements based on genetic algorithms , 2005, Comput. Vis. Image Underst..

[2]  Padhraic Smyth,et al.  Business applications of data mining , 2002, CACM.

[3]  El-Ghazali Talbi,et al.  A parallel genetic algorithm for rule mining , 2001, Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001.

[4]  Usama M. Fayyad,et al.  Data mining and knowledge discovery in databases: implications for scientific databases , 1997, Proceedings. Ninth International Conference on Scientific and Statistical Database Management (Cat. No.97TB100150).

[5]  Frank Y. Shih,et al.  Enhancement of image watermark retrieval based on genetic algorithms , 2005, J. Vis. Commun. Image Represent..

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  Xiaoming Chen,et al.  Using an Interest Ontology for Improved Support in Rule Mining , 2003, DaWaK.

[8]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[9]  Witold Pedrycz,et al.  Genetically optimized fuzzy decision trees , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Frank Y. Shih,et al.  An adjusted-purpose digital watermarking technique , 2004, Pattern Recognit..

[11]  Frank Y. Shih,et al.  Genetic algorithm based methodology for breaking the steganalytic systems , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Donald K. Wedding,et al.  Discovering Knowledge in Data, an Introduction to Data Mining , 2005, Inf. Process. Manag..

[13]  Nasser Kehtarnavaz,et al.  DSP-based hierarchical neural network modulation signal classification , 2003, IEEE Trans. Neural Networks.

[14]  Konstantina S. Nikita,et al.  Characterization of CT liver lesions based on texture features and a multiple neural network classification scheme , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[15]  Kyuseok Shim,et al.  Mining Sequential Patterns with Regular Expression Constraints , 2002, IEEE Trans. Knowl. Data Eng..