Hybrid Learning Using Multi-objective Genetic Algorithms and Decision Trees for Power Quality Disturbance Pattern Recognition

The objective of this work is to exploit the potential of latest pattern recognition techniques in power quality applications. This paper presents a novel hybrid pattern recognizer for classification of power quality disturbances. The hybrid learning methodology integrates a multiobjective genetic algorithm (GA) and decision trees (CART) in order to evolve optimal subsets of discriminatory features for robust pattern classification. In the training phase the multiobjective GA based on the wrapper approach is used to find a subset of relevant attributes that minimizes both classification error rate and size of the tree discovered by the classification algorithm, namely CART, using the Pareto dominance approach. For a given feature subset, CART is invoked to produce a decision tree; the classification error and the complexity of the decision tree are used as the fitness functions by the GA to evolve better subsets. Experimental results reveal that the proposed multiobjective GA-CART combined approach yields improved classification performance and reduced classification time as compared to standard CART decision trees.