Adaptive genetic algorithm for reliable training population in plant breeding genomic selection

Abstract

Many algorithms are developed to model Genomic Estimated Breeding Value (GEBV). Modeling GEBV evolves a huge size of genotype in both terms of the dimension (columns) and the instances (rows). Good combinations of features help in predicting which phenotype is being represented. Preparing a good training population sample is assumed to be a convenient solution to deal with such complex genotype data. In this research, an Adaptive Genetic Algorithm (AGA) is proposed. The adaptive characteristic of AGA by adjusting probabilities in crossover and mutation is expected to converge into the global optimum without getting trapped in local optima. The proposed method using AGA to optimize the feature selection and shrinkage mechanism is looked forward to provide a reliable model to be reused in other similar datasets.