Gene Expression Programming - a new machine learning technique for supervised and unsupervised classification

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Many scientific, engineering and business fields such as genetics, medicine, environment science and engineering, physics, astronomy, finance, and marketing are facing common challenges in dealing with complex data for extracting field-specific knowledge. Efficient data analysis techniques are needed in order to intelligently assist the user in extracting this knowledge. This project will address this need using the basic ideas of a recently developed computer algorithm, Gene Expression Programming, for the development of novel evolutionary algorithms techniques and novel supervised and unsupervised data classification algorithms. The project will develop and exploit novel homologous genetic operators, and mechanisms to control the redundant information in the solutions provided by the algorithm in order to increase its efficiency. These developments will be combined with state-of-the-art statistical methods such as boosting learning in order to create efficient data classification algorithms.The methods and algorithms developed in the project will be implemented in software applications made available as open-source in order to maximize the spectrum of the beneficiaries of the project outcomes.