Langdon, WB;
Banzhaf, W;
(2022)
Long-term evolution experiment with genetic programming [hot of the press].
In:
GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion.
(pp. pp. 29-30).
ACM
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Abstract
We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by their depth and suggest deep expressions are resilient to learning as they disperse information, impeding evolvability and the adaptation of highly nested organisms and instead we argue for open complexity. Programs with more than 2 000 000 000 instructions (depth 20 000) are created by crossover. To support unbounded long-term evolution experiments LTEE in GP we use incremental fitness evaluation and both SIMD parallel AVX 512 bit instructions and 16 threads to yield performance equivalent of up to 1.1 trillion GP operations per second, 1.1 tera-GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server.
| Type: | Proceedings paper |
|---|---|
| Title: | Long-term evolution experiment with genetic programming [hot of the press] |
| Event: | GECCO '22: Genetic and Evolutionary Computation Conference |
| ISBN-13: | 9781450392686 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3520304.3534065 |
| Publisher version: | https://doi.org/10.1145/3520304.3534065 |
| 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: | genetic programming, Information Theory limit on complexity, Long-Term Evolution Experiment LTEE, extended unlimited evolution, Open Complexity, Speedup technique |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10155482 |
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