Optimizing the equation for a dataset with corresponding attributes by hybrid genetic algorithm

Genetic algorithm is a programming technique that mimics biological evolution as a problem-solving strategy and being applied to a broad range of subjects. In this study hybrid genetic algorithm is used to optimize the equation for a dataset with corresponding attribute. This new approach uses local optimizer in genetic algorithm; thus, the algorithm attains more speed and accuracy. This study shows that, when the attributes are related to each other, hybrid genetic algorithm is more successful than regression methods at finding target equation. The evaluated equation can be applied on a real world dataset to find relations between attributes, and then, evaluated equation can be used for classification over corresponding dataset.