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Visualising the Search Landscape of the Triangle Program

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Genetic Programming (EuroGP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10196))

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Abstract

High order mutation analysis of a software engineering benchmark, including schema and local optima networks, suggests program improvements may not be as hard to find as is often assumed. (1) Bit-wise genetic building blocks are not deceptive and can lead to all global optima. (2) There are many neutral networks, plateaux and local optima, nevertheless in most cases near the human written C source code there are hill climbing routes including neutral moves to solutions.

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Notes

  1. 1.

    http://geneticimprovementofsoftware.com/?page_id=13 (accessed Oct, 9 2016).

  2. 2.

    A \(16^\mathrm{th}\) order schema has 16 defined positions [64, page 29], and one variable * position (length = 17). Whereas a \(1^\mathrm{st}\) order mutation is identical to the original except for one change.

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  1. CREST, Computer Science, UCL, London, WC1E 6BT, UK

    William B. Langdon

  2. Computing Science and Mathematics, University of Stirling, Stirling, FK9 4LA, UK

    Nadarajen Veerapen & Gabriela Ochoa

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  2. Universidade Nova de Lisboa , Lisbon, Portugal

    Mauro Castelli

  3. Brno University of Technology , Brno, Czech Republic

    Lukas Sekanina

  4. Vrije Universiteit Amsterdam , Amsterdam, The Netherlands

    Evert Haasdijk

  5. University of Cádiz , Cádiz, Spain

    Pablo García-Sánchez

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Langdon, W.B., Veerapen, N., Ochoa, G. (2017). Visualising the Search Landscape of the Triangle Program. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2017. Lecture Notes in Computer Science(), vol 10196. Springer, Cham. https://doi.org/10.1007/978-3-319-55696-3_7

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