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Genetic programming convergence

Langdon, WB; (2022) Genetic programming convergence. In: GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference. (pp. pp. 27-28). ACM: New YorkNYUnited States. Green open access

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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. W. B. Langdon. 2022. Genetic Programming Convergence. GP & EM 23, 1, 71 - 104.

Type: Proceedings paper
Title: Genetic programming convergence
Event: GECCO '22: Genetic and Evolutionary Computation Conference
ISBN-13: 9781450392686
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3520304.3534063
Publisher version: https://doi.org/10.1145/3520304.3534063
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, evolutionary computation, stochastic search, diversity, bottom up incremental evaluation, PIE, propagation, Failed Disruption Propagation, FDP, 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/10155481
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