Using Particle Swarm Optimization and Genetic Programming to Evolve Classification Rules

According to analyzing particle swarm optimization (PSO), the structure of genetic programming (GP) and classifier model, PSO algorithm and GP were made to combine to evolve classification rules. Rules were described as binary tree which non-leaf node denoted rule structure and leaf-node was correspond to rule value. Leaf node and non-leaf node employed different evolutionary strategy. First, PSO was applied to evolve leaf node in order to obtain the optimum rule of certain structure, then GP was adopted to optimize rule structure. The best rules were obtained after the twice optimization. Finally, the new method indicated efficiency through experiments on several datasets of UCI