Multiobjective optimization in Gene Expression Programming for Dew Point

The processes occurring in climatic change evolution and their variations play a major role in environmental engineering. Different techniques are used to model the relationship between temperatures, dew point and relative humidity. Gene expression programming is capable of modelling complex realities with great accuracy, allowing, at the same time, the extraction of knowledge from the evolved models compared to other learning algorithms. This research aims to use Gene Expression Programming for modelling of dew point. Generally, accuracy of the model is the only objective used by selection mechanism of GEP. This will evolve large size models with low training error. To avoid this situation, use of multiple objectives, like accuracy and size of the model are preferred by Genetic Programming practitioners. Multi-objective problem finds a set of solutions satisfying the objectives given by decision maker. Multiobjective based GEP will be used to evolve simple models. Various algorithms widely used for multi objective optimization like NSGA II and SPEA 2 are tested for different test cases. The results obtained thereafter gives idea that SPEA 2 is better algorithm compared to NSGA II based on the features like execution time, number of solutions obtained and convergence rate. Thus compared to models obtained by GEP, multi-objective algorithms fetch better solutions considering the dual objectives of fitness and size of the equation. These simple models can be used to predict dew point.