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Incremental Evaluation in Genetic Programming

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Genetic Programming (EuroGP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12691))

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Abstract

Often GP evolves side effect free trees. These pure functional expressions can be evaluated in any order. In particular they can be interpreted from the genetic modification point outwards. Incremental evaluation exploits the fact that: in highly evolved children the semantic difference between child and parent falls with distance from the syntactic disruption (e.g. crossover point) and can reach zero before the whole child has been interpreted. If so, its fitness is identical to its parent (mum).

Considerable savings in bloated binary tree GP runs are given by exploiting population convergence with existing GPquick data structures, leading to near linear O(gens) runtime. With multi-threading and SIMD AVX parallel computing a 16 core desktop can deliver the equivalent of 571 billion GP operations per second, 571 giga GPop/s.

GP convergence is viewed via information theory as evolving a smooth landscape and software plasticity. Which gives rise to functional resilience to source code changes. On average a mixture of 100 +, −, \(\times \) and (protected) \(\div \) tree nodes remove test case effectiveness at exposing changes and so fail to propagate crossover infected errors.

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Notes

  1. 1.

    (DIV X Y) is protected division which returns 1.0 if Y = 0 and X/Y otherwise. Ni et al. [18] propose the analytic quotient instead of DIV.

  2. 2.

    Trapping special cases, such as multiplication by zero, and multiplication or division by one, only sped up GP by a few percent.

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Acknowledgements

Funded by EPSRC grant EP/P005888/1.

For GPinc C++ code see my home pages.

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Authors and Affiliations

  1. CREST, Department of Computer Science, UCL, Gower Street, London, WC1E 6BT, UK

    William B. Langdon

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  1. William B. Langdon
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Correspondence to William B. Langdon .

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Editors and Affiliations

  1. Queen’s University, Kingston, ON, Canada

    Ting Hu

  2. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  3. University of Trieste, Trieste, Italy

    Eric Medvet

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Langdon, W.B. (2021). Incremental Evaluation in Genetic Programming. In: Hu, T., Lourenço, N., Medvet, E. (eds) Genetic Programming. EuroGP 2021. Lecture Notes in Computer Science(), vol 12691. Springer, Cham. https://doi.org/10.1007/978-3-030-72812-0_15

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