research-article

A genetic programming based hyper-heuristic approach for combinatorial optimisation

Authors:

Mark JohnstonAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1299 - 1306

https://doi.org/10.1145/2001576.2001752

Published: 12 July 2011 Publication History

Abstract

Genetic programming based hyper-heuristics (GPHH) have become popular over the last few years. Most of these proposed GPHH methods have focused on heuristic generation. This study investigates a new application of genetic programming (GP) in the field of hyper-heuristics and proposes a method called GPAM, which employs GP to evolve adaptive mechanisms (AM) to solve hard optimisation problems. The advantage of this method over other heuristic selection methods is the ability of evolved adaptive mechanisms to contain complicated combinations of heuristics and utilise problem solving states for heuristic selection. The method is tested on three problem domains and the results show that GPAM is very competitive when compared with existing hyper-heuristics. An analysis is also provided to gain more understanding of the proposed method.

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Cited By

Vela AValencia-Rivera GCruz-Duarte JOrtiz-Bayliss JAmaya I(2025)Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance MetricsIEEE Access10.1109/ACCESS.2025.353220113(14983-14997)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3532201
Deng JZhang JYang S(2024)A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup timesEngineering Optimization10.1080/0305215X.2024.233528457:3(786-812)Online publication date: 23-Apr-2024
https://doi.org/10.1080/0305215X.2024.2335284
Chitty D(2024)Strategies to Apply Genetic Programming Directly to the Traveling Salesman ProblemAdvances in Computational Intelligence Systems10.1007/978-3-031-47508-5_25(311-324)Online publication date: 1-Feb-2024
https://doi.org/10.1007/978-3-031-47508-5_25
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Index Terms

A genetic programming based hyper-heuristic approach for combinatorial optimisation
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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https://doi.org/10.1109/ACCESS.2025.3532201
Deng JZhang JYang S(2024)A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup timesEngineering Optimization10.1080/0305215X.2024.233528457:3(786-812)Online publication date: 23-Apr-2024
https://doi.org/10.1080/0305215X.2024.2335284
Chitty D(2024)Strategies to Apply Genetic Programming Directly to the Traveling Salesman ProblemAdvances in Computational Intelligence Systems10.1007/978-3-031-47508-5_25(311-324)Online publication date: 1-Feb-2024
https://doi.org/10.1007/978-3-031-47508-5_25
Chitty DKeedwell ESilva SPaquete L(2023)Phased Genetic Programming for Application to the Traveling Salesman ProblemProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3590673(547-550)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583133.3590673
Vela ACruz-Duarte JOrtiz-Bayliss JAmaya I(2023)Recursive Hyper-Heuristics for the Job Shop Scheduling Problem2023 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC53210.2023.10254151(1-8)Online publication date: 1-Jul-2023
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Hiruta YNishihara KKoguma YFujii MNakata M(2022)Automatic Construction of Loading Algorithms With Interactive Genetic ProgrammingIEEE Access10.1109/ACCESS.2022.322554310(125167-125180)Online publication date: 2022
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Vela ACruz-Duarte JOrtiz-Bayliss JAmaya I(2022)Beyond Hyper-Heuristics: A Squared Hyper-Heuristic Model for Solving Job Shop Scheduling ProblemsIEEE Access10.1109/ACCESS.2022.316950310(43981-44007)Online publication date: 2022
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Kieffer EDuflo GDanoy GVarrette SBouvry P(2022)A RNN-Based Hyper-heuristic for Combinatorial ProblemsEvolutionary Computation in Combinatorial Optimization10.1007/978-3-031-04148-8_2(17-32)Online publication date: 4-Apr-2022
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Masood AChen GZhang M(2021)Feature Selection for Evolving Many-Objective Job Shop Scheduling Dispatching Rules with Genetic Programming2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504895(644-651)Online publication date: 28-Jun-2021
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Kheiri A(2020)Heuristic Sequence Selection for Inventory Routing ProblemTransportation Science10.1287/trsc.2019.093454:2(302-312)Online publication date: 9-Mar-2020
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