Framework for discovery of data models using genetic programming

Abstract

The field of Genetic Programming in Artificial Intelligence strives to get computers to solve a problem without explicitly coding a solution by a programmer. Genetic Programming is a relatively new technology, which comes under automatic programming. After the initial work by John R. Koza in genetic programming, much research work have been done to discover data models in various datasets. These work have been rather domain specific and little attention has been given to develop generic framework for modeling and experimenting with genetic programming solutions for real world problems. This paper discusses a project to develop a visual environment, named as GPVLab, to design and experiment with genetic programming solutions for real world problems. GPVLab has successfully discovered data models for various data sets and according to the main evaluation it is evident that GPVLab can generate solutions which provide better results in 56% of the time. It is concluded that GPVLab can be used to model genetic programming application very conveniently. GPVLab can be used not only for discovering data models but also doing various experiments in genetic programming.