Selection of fitness function in genetic programming for binary classification

Abstract

Fitness function is a key parameter in genetic programming (GP) and is also known as the driving force of GP. It determines how well a solution is able to solve the given problem. The design of fitness function is instrumental in performance improvement of GP. In this study we evaluate different fitness functions for binary classification using two benchmarking datasets. Two types of fitness functions are used. One type uses statistical distribution of classes in the datasets and the other uses machine learning classifiers. A detailed analysis and comparison are given between different fitness functions in terms of performance and computational complexity.