Langdon, William B;
Clark, David;
(2025)
Population Diversity, Information Theory and Genetic Improvement.
In: Xue, Bing and Manzoni, Luca and Bakurov, Illya, (eds.)
Genetic Programming (EuroGP 2025).
(pp. pp. 85-102).
Springer: Cham, Switzerland.
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Abstract
Compression, e.g. gzip, gives algorithmic information theory (Kolmogorov Complexity) based measures of string population diversity. To boost it we use the GI tool Magpie and select programs of average fitness that contribute most to variety, allowing evolution to automatically tailor triangle.c for production speed. We calculate C source code diversity via approximations to the Normalised Compression Distance on Multisets (NCDm) using both Cohen and Vitanyi’s O(n2) approach and our own, O(n) method, finding the cheaper, O(n), is equally good.
| Type: | Proceedings paper |
|---|---|
| Title: | Population Diversity, Information Theory and Genetic Improvement |
| Event: | 28th European Conference, EuroGP 2025 |
| ISBN-13: | 978-3-031-89990-4 |
| DOI: | 10.1007/978-3-031-89991-1_6 |
| Publisher version: | https://doi.org/10.1007/978-3-031-89991-1_6 |
| 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. |
| Keywords: | Evolutionary computing, EC, genetic programming, GP, SBSE, NCD, Normalised Information Distance, NID, perf, test set diameter |
| 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/10208413 |
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