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Strategies to Apply Genetic Programming Directly to the Traveling Salesman Problem

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Advances in Computational Intelligence Systems (UKCI 2023)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1453))

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Abstract

Genetic Programming (GP) is an evolutionary methodology for generating programs typically applied to classification and symbolic regression problems. GP is not ordinarily applied directly to solve combinatorial optimisation problems. However, GP can be considered similar to hyper-heuristic methods which apply simple heuristics sequentially to a given solution to a problem, a set of operations or a program. Consequently, this paper will present a novel implementation of GP which can directly solve optimisation problems. Similar to hyper-heuristics, a hill-climbing method to GP is presented whereby programs are constructed in small parts or phases, Phased-GP. Furthermore, acceptance strategies for the use of Phased-GP are explored to improve its performance. When Phased-GP is applied directly to Traveling Salesman Problems of up to 1000 cites solutions within 6% of optimal can be derived using only simple operators, a significant improvement over standard GP.

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Author information

Authors and Affiliations

  1. Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QF, UK

    Darren M. Chitty

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  1. Darren M. Chitty
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Correspondence to Darren M. Chitty .

Editor information

Editors and Affiliations

  1. Aston University, Birmingham, UK

    Nitin Naik

  2. Cardiff Metropolitan University, Cardiff, UK

    Paul Jenkins

  3. Aston University, Birmingham, UK

    Paul Grace

  4. Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

  5. Devi Ahilya University, Indore, India

    Shaligram Prajapat

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Chitty, D.M. (2024). Strategies to Apply Genetic Programming Directly to the Traveling Salesman Problem. In: Naik, N., Jenkins, P., Grace, P., Yang, L., Prajapat, S. (eds) Advances in Computational Intelligence Systems. UKCI 2023. Advances in Intelligent Systems and Computing, vol 1453. Springer, Cham. https://doi.org/10.1007/978-3-031-47508-5_25

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