A feature selection method based on adaptive simulated annealing genetic algorithm

Feature selection is one of important problems in machine learning and pattern recognition areas. For high demensian data, feature dimension can be decreased under the condition of ensuring data integrity and classification accuracy can be improved by feature selection. A feature selection method based on adaptive simulated annealing genetic algorithm was proposed, which embeds the simulated annealing algorithm in the circle of adaptive genetic algorithm and uses the feature that simulated annealing algorithm has the strong ability of local search and makes searching process avoid sinking into the local optimal solution, to solve the shortcomings of slow convergence speed and high time complexity. The experiment results show that the method can guarantee the correct rate of classification and improve the efficiency of feature selection.