Data Sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data

Yuri Lavinas, Nathan Haut, William Punch, Wolfgang Banzhaf, Sylvain Cussat-Blanc

Abstract: In this contribution, we explore Cartesian Genetic Programming for image analysis of biomedical data. Producing large quantities of human-labeled biomedical data is an expensive task. Here, we introduce a way for CGP to use a small amount of training data, without loss in performance. To define the size of the training data, we utilize an Active Learning method to direct the algorithm towards informative samples. We examine how sampling a small set of data from the CELLPOSE dataset affects the performance of CGP. We also study the effects of restarting CGP with Active Learning. We found that using several restarts can lead to a more diverse set of the highest-performing solutions with fewer active nodes while maintaining similar performance to standard CGP.

External IDs:dblp:conf/cec/LavinasHPBC24