  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.
