Visualizing the Semantic Space Exploration of Ensemble Methods

Created by W.Langdon from gp-bibliography.bib Revision:1.9225

@InProceedings{Santos:2026:GECCOcomp,

• author = "Frederico J. J. B. Santos and Jose {Munoz Contreras} and Berfin Sakallioglu and Damaris Hualinga and Alberto Tonda and Leonardo Trujillo",
• title = "Visualizing the Semantic Space Exploration of Ensemble Methods",
• booktitle = "GECCO 2026 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• year = "2026",
• editor = "Nelishia Pillay and Gabriel Kronberger and Leonardo Trujillo and Ting Hu and Sebastian {Rojas Gonzalez} and Saul {Calderon Ramirez} and Stephan Winkler and Yazmin Maldonado and James McDermott and Jose Manuel {Munoz Contreras} and Giorgia Nadizar and Marcella {Scoczynski Ribeiro Martins} and Hirad Assimi and Mario Andres Munoz and Oliver Cuate and Daniel Hernandez and Yanan Sun and Edgar Galvan and Aniko Ekart and Nadarajen Veerapen",
• pages = "229--232",
• address = "San Jose, Costa Rica",
• series = "GECCO '26",
• month = "13-17 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, dimensionality reduction, GSGP, ensemble learning, semantic space, symbolic regression",
• isbn13 = "9798400724886",
• DOI = "10.1145/3795101.3805402",
• size = "4 pages",
• abstract = "Ensembles of decision trees, state-of-the-art for regression, share conceptual similarities with Geometric Semantic Genetic Programming (GSGP). Both combine weak learners to build a final ensemble. Boosting algorithms (XGBoost, LightGBM, Gradient Boosting) fit new decision trees to compensate for current errors, while GSGP builds ensembles incrementally using stochastic mutations toward target semantics. Other methods generate weak models in batch, ensuring diversity via bagging (Random Forest) or increased randomness (Extremely Randomized Trees). This paper explores visualization strategies to analyze how these methods navigate semantic space during learning. Kernel PCA projects high-dimensional semantics into 2D representations. Heatmaps show fitness distributions; density plots illustrate which regions each method visits most frequently. For incremental methods, trajectory visualizations capture the sequence of semantic points visited during learning. Qualitative analysis reveals distinct patterns: batch ensembles cluster tightly near target semantics; boosting methods follow directed incremental paths; GSGP explores more broadly than any other method. These insights suggest opportunities for hybrid approaches combining GSGP's exploration with the efficient exploitation of decision tree based methods.",
• notes = "GECCO-2026 A Recombination of the 35th International Conference on Genetic Algorithms (ICGA) and the 31st Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Frederico J J B Santos Jose Manuel Munoz Contreras Berfin Sakallioglu Damaris Hualinga Alberto Tonda Leonardo Trujillo

Citations