Genetic Algorithm Based Artificial Bee Colony Algorithm for Grid Scheduling

Abstract: Lung cancer is one of the most dangerous cancer’s type in the world. Early detection can save the life and survivability of the patients. In this paper we want to propose a solution in the direction of lung cancer symptom detection. In this paper we proposed a new algorithm that is support based ant colony optimization technique (SPACO). Our algorithm is broadly divided into three parts, in the first part we accept the data set of cancer symptoms which is a generalized way for creating the patterns for Lung Cancer Framework, and in the second part we find the relevant data from the patterns. We can choose the frequent symptoms only by using the support count value. According to the support value we decide the ants and pheromone value. We initialize the pheromone value which is the support of the pattern of cancer symptoms. It is updated in each trial. By updating the pheromone value in each step we can check the symptom quality which either increases the prediction or decreases the prediction. Finally by result analysis we can prove the effectiveness of our algorithm.

Keywords: SPACO, ACO, data mining, rule pruning, Pheromone