Abstract:
Genetic Programming (GP) is a technique that deals with evolving computer
programs using biologically inspired methods. GP is a set of instruction and a fitness function
to evaluate the best solution. The objective of GP is to find a computer program capable of
solving a predefined problem. GP has capability to select the useful features for the new
generation and discard the unwanted features during evolution. In this paper, GP is used for
real world classification problems. Five real world problems are used to evaluate the GP
performance. In this paper, Gaussian Distribution Criteria, Standard Accuracy Method,
Average Class Accuracy Method and Artificial Neural Networks (ANN) are used for the
evaluation of fitness function for binary classification problems. A number of experiments
are carried out to evaluate and compare the results obtained from GP. Results prove that GP
(ANN) provide a better accuracy as compared to others methods.
