Attribute reduction approach is proposed in this paper based on a modified version of the flower pollination algorithm optimization (FPA). Flower pollination algorithm (FPA) is one of recently evolutionary computation technique, inspired by the pollination process of flowers. The modified FPA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution through using local searching with adaptive search diversity. The modified FPA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system is applied on a eight dataset from the UCI machine learning data sets and proves a good performance in comparison with the genetic algorithm (GA) and particle swarm optimization (PSO), that commonly used in this context.