|Adapted Geometric Semantic Genetic Programming For Diabetes And Breast Cancer Classification|
|Zhechen Zhu, Asoke K. Nandi, Muhammad Waqar Aslam|
In this paper, we explore new Adapted Geometric Semantic (AGS) operators in the case where Genetic programming (GP) is used as a feature generator for signal classiﬁcation. Also to control the computational complexity, a devolution
scheme is introduced to reduce the solution complexity without any signiﬁcant impact on their ﬁtness. Fisher"s criterion is employed as ﬁtness function in GP. The proposed method is tested using diabetes and breast cancer datasets. According to the experimental results, GP with AGS operators and devolution mechanism provides better classiﬁcation performance while requiring less training time as compared to standard GP.