Considering Reputation in the Selection Strategy of Genetic Programming

Abstract

Genetic programming (GP) is an evolutionary algorithm inspired by biological evolution. GP has shown to be effective to build prediction and classification model with high accuracy. Individuals in GP are evaluated by fitness, which serves as the basis of selection strategy: GP selects individuals for reproducing their offspring based on fitness. In addition to fitness, this study considers the reputation of individuals in the selection strategy of GP. Reputation is commonly used in social networks, where users earn reputation from others through recognized performance or effort. In this study, we define the reputation of an individual according to its potential to produce good offspring. Therefore, selecting parents with high reputation is expected to increase the opportunity for generating good candidate solutions. This study applies the proposed algorithm, called the RepGP, to solve the classification problems. Experimental results on four data sets show that RepGP with certain degrees of consanguinity can outperform two GP algorithms in terms of classification accuracy, precision, and recall.KeywordsParent SelectionHigh ReputationTraining AccuracySurvival SelectionGenetic Programming AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.