Open-Ended Evolution with Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8476

@InProceedings{Langdon:2025:IMOL,

• author = "William B. Langdon",
• title = "Open-Ended Evolution with Linear Genetic Programming",
• booktitle = "The 7th International Workshop on Intrinsically Motivated Open-ended Learning (IMOL 2025)",
• year = "2025",
• editor = "Nicola Catenacci Volpi and Christoph Salge and Daniel Polani and Gianluca Baldassarre and Vieri Giuliano Santucci and Cedric Colas and Kenzo Clauw and Erik Lintunen and Bente Riegler and Jan Kim",
• address = "University of Hertfordshire",
• month = "8-10 " # sep,
• keywords = "genetic algorithms, genetic programming, Autonomous open-ended learning in machines, LTEE, Voas PIE, information theory, failed disruption propagation, catalyst computing, skin depth, thin skinned software: Poster",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Langdon_2025_IMOL.pdf",
• code_url = "https://github.com/wblangdon/GPengine",
• size = "4 pages",
• abstract = "Inspired by Richard Lenski's Long-Term Evolution Experiment, we use the quantised chaotic Mackey-Glass time series as a prolonged learning task for artificial intelligence in the form of steady state linear genetic programming using GPengine to reach up to 100000 generations. Using two point crossover and point mutation we evolve programs of up to 4 million instructions. Typically finding hundreds of fitness improvements in the later stages of the runs.",
• notes = "https://imol2025.github.io/",

}

Genetic Programming entries for William B Langdon

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