Langdon, WB;
(2021)
Genetic programming convergence.
Genetic Programming and Evolvable Machines
, 23
pp. 71-104.
10.1007/s10710-021-09405-9.
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Abstract
We study both genotypic and phenotypic convergence in GP floating point continuous domain symbolic regression over thousands of generations. Subtree fitness variation across the population is measured and shown in many cases to fall. In an expanding region about the root node, both genetic opcodes and function evaluation values are identical or nearly identical. Bottom up (leaf to root) analysis shows both syntactic and semantic (including entropy) similarity expand from the outermost node. Despite large regions of zero variation, fitness continues to evolve and near zero crossover disruption suggests improved GP systems within existing memory use.
| Type: | Article |
|---|---|
| Title: | Genetic programming convergence |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10710-021-09405-9 |
| Publisher version: | https://doi.org/10.1007/s10710-021-09405-9 |
| 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 computation, stochastic search, diversity, bottom up incremental evaluation, PIE, propagation, infection, and execution, SIMD parallel processing, AVX vector instructions |
| 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/10146198 |
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