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A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction

Author

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  • Jana Ries

    (University of Portsmouth, Hampshire, UK)

  • Patrick Beullens

    (University of Southampton, Southampton, UK)

Abstract

Two main concepts are established in the literature for the Parameter Setting Problem of metaheuristics: Parameter Tuning Strategies (PTS) and Parameter Control Strategies (PCS). While PTS result in a fixed parameter setting for a set of problem instances, PCS are incorporated into the metaheuristic and adapt parameter values according to instance-specific performance feedback. The idea of Instance-specific Parameter Tuning Strategies (IPTS) is aiming to combine advantages of both tuning and control strategies by enabling the adoption of parameter values tailored to instance-specific characteristics a priori to running the metaheuristic. This requires, however, a significant knowledge about the impact of instance characteristics on heuristic performance. This paper presents an approach that semi-automatically designs the fuzzy logic rule base to obtain instance-specific parameter values by means of decision trees. This enables the user to automate the process of converting insights about instance-specific information and its impact on heuristic performance into a fuzzy rule base IPTS system. The system incorporates the decision maker’s preference about the trade-off between computational time and solution quality.

Suggested Citation

  • Jana Ries & Patrick Beullens, 2015. "A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(5), pages 782-793, May.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:5:p:782-793
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    Cited by:

    1. José García & Paola Moraga & Matias Valenzuela & Hernan Pinto, 2020. "A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    2. José García & Victor Yepes & José V. Martí, 2020. "A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    3. Mauricio Castillo & Ricardo Soto & Broderick Crawford & Carlos Castro & Rodrigo Olivares, 2021. "A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models," Mathematics, MDPI, vol. 9(12), pages 1-21, June.
    4. José García & José V. Martí & Víctor Yepes, 2020. "The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm," Mathematics, MDPI, vol. 8(6), pages 1-22, May.
    5. Mohammad Asghari & Seyed Mohammad Javad Mirzapour Al-E-Hashem & Yacine Rekik, 2022. "Environmental and social implications of incorporating carpooling service on a customized bus system," Post-Print hal-03598768, HAL.

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