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Genetic programming hyperheuristic parameter configuration using fitness landscape analysis

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Abstract

Fitness landscape analysis is a tool that can help us gain insight into a problem, determine how hard it is to solve a problem using a given algorithm, choose an algorithm for solving a given problem, or choose good algorithm parameters for solving the problem. In this paper, fitness landscape analysis of hyperheuristics is used for clustering instances of three scheduling problems. After that, good parameters for tree-based genetic programming that can solve a given scheduling problem are calculated automatically for every cluster. Additionally, we introduce tree editing operators which help in the calculation of fitness landscape features in tree based genetic programming. A heuristic is proposed based on introduced operators, and it calculates the distance between any two trees. The results show that the proposed approach can obtain parameters that offer better performance compared to manual parameter selection.

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Availability of data and material

The data for the resource constrained project scheduling problem (RCPSP) is a part of the PSPLIB library, which is opensource and available online. The data for single machine environment and unrelated machines environment is available on request.

Notes

  1. http://www.om-db.wi.tum.de/psplib/getdata_sm.html

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Funding

This work has been partially supported by the Croatian Science Fundation under the project IP-2019-04-4333, which is titled Hyperheuristic Design of Dispatching Rules.

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Authors and Affiliations

  1. Department of Mathematics, J.J. Strossmayer University of Osijek, Gajev trg 6, 31000, Osijek, Croatia

    Rebeka Čorić & Mateja Ðumić

  2. Faculty of Electrical Engineering and Computing, Unska 3, 10000, Zagreb, Croatia

    Domagoj Jakobović

Authors
  1. Rebeka Čorić
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  2. Mateja Ðumić
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  3. Domagoj Jakobović
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Correspondence to Rebeka Čorić.

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Čorić, R., Ðumić, M. & Jakobović, D. Genetic programming hyperheuristic parameter configuration using fitness landscape analysis. Appl Intell 51, 7402–7426 (2021). https://doi.org/10.1007/s10489-021-02227-3

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