A Hybrid Metaheuristic Algorithm for Classification using Micro array Data
نویسندگان
چکیده
A metaheuristic algorithms provide effective methods to solve complex problems using f inite sequence of instructions. It can be defined as an iterative search process that eff iciently performs the exploration and exploitation in the solution space aiming to eff icient ly f ind near optimal solutions. This iterative process has adopted various natural intelligences and aspirations. In this work, to f ind optimal solutions for microarray data, nature-inspired metaheuristic algorithms w ere adapted. A Flexible Neural Tree (FNT) model for microarray data is created using nature-inspired algorithms. The structure of FNT is created using the Ant Colony Optimization (ACO) and the parameters encoded in the neural tree are optimized by Firefly Algorithm (FA). FA is used to produce near optimal solutions and hence it is superior to the existing metaheuristic algorithm. Experimental results w ere analyzed in terms of accuracy and error rate to converge to the optimum. The proposed model is compared w ith other models for evaluating its performance to f ind the appropriate model.
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