Association Rule Optimize By Multi Objective Genetic Algorithm

Association analysis is the task of bringing out relationship among data. Association analysis is most popular analysis technique in data mining for classifying biological data. When dealing with Biological data large search spaces may arise. Genetic algorithms deal with large search space very effectively. Combining association rule mining and Genetic algorithms to classify biological data is a novel and extensive research area. The purpose of this thesis is to classify biological data with association rule and genetic algorithm. A Genetic Algorithm (GA) is an iterative search, optimization and adaptive machine learning technique premised on the principles of Natural Selection. A GA is a search method that functions analogously to an evolutionary process in a biological system as it mimics evolution and competition between individuals in natural selection. It generates a better solution from existing solutions. Neither programmer nor genetic algorithm has to know how to solve a given problem; solution is just bred. Gas is one of the most robust problem solving techniques. They can find solutions of NP-hard problems easily. For problems with a larger parameter space and where the problem itself can be easily specified, GA can be an appropriate solution. In this paper described all the method of association rule mining with genetic algorithm. “Genetic Algorithms are software procedures modeled after genetics and evolution� Index Terms Ensemble System, Association, Classification.