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

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{lavinas:2024:CEC,

• author = "Yuri Lavinas and Nathan Haut and William Punch and Wolfgang Banzhaf and Sylvain Cussat-Blanc",
• title = "Data Sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Uncertainty, Shape, Image color analysis, Training data, Bioinformatics, Task analysis, image analysis, image processing, data sampling",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10611879",
• 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 use 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.",
• notes = "also known as \cite{10611879}

  WCCI 2024",

}

Genetic Programming entries for Yuri Lavinas Nathaniel Haut William F Punch Wolfgang Banzhaf Sylvain Cussat-Blanc

Citations