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.