Langdon, William B;
(2025)
Open-Ended Evolution with Linear Genetic Programming.
In:
Proceedings of the 7th International Workshop on Intrinsically Motivated Open-ended Learning (IMOL 2025).
IMOL: Hatfield, UK.
(In press).
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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 100 000 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.
| Type: | Proceedings paper |
|---|---|
| Title: | Open-Ended Evolution with Linear Genetic Programming |
| Event: | The 7th International Workshop on Intrinsically Motivated Open-ended Learning (IMOL 2025) |
| Location: | University of Hertfordshire |
| Dates: | 8 Sep 2025 - 10 Sep 2025 |
| Publisher version: | https://imol2025.github.io/ |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215042 |
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