Publications Operational Research Group

2018

Refereed Journal Papers

  • A Choice Function Hyper-heuristic Framework for the Allocation of Maintenance Tasks in Danish Railways
    Shahrzad M. Pour, John H. Drake, and Edmund K. Burke
    Computers & Operations Research, Volume 93, pp. 15–26, May 2018.
    [bib] [doi
  • A Hybrid Dynamic Programming and Memetic Algorithm to the Traveling Salesman Problem With Hotel Selection
    Yongliang Lu, Una Benlic, and Qinghua Wu
    Computers & Operations Research, Volume 90, pp. 193–207, February 2018.
    [bib] [doi
  • Heuristic Search for Allocation of Slots at Network Level
    Una Benlic
    Transportation Research Part C: Emerging Technologies, Volume 86, pp. 488–509, January 2018.
    [bib] [doi
  • A Hyper-heuristic Approach to Automated Generation of Mutation Operators for Evolutionary Programming
    Libin Hong, John H. Drake, John R. Woodward, and Ender Özcan
    Applied Soft Computing, Volume 62, pp. 162–175, January 2018.
    [bib] [doi
  • A Hybrid Constraint Programming/Mixed Integer Programming Framework for the Preventive Signaling Maintenance Crew Scheduling Problem
    Shahrzad M. Pour, John H. Drake, Lena Secher Ejlertsen, Kourosh Marjani Rasmussen, and Edmund K. Burke
    European Journal of Operational Research, 2018. [To Appear].
    [bib] [doi
  • Pruning Rules for Optimal Runway Sequencing
    Geert De Maere, Jason A. D. Atkin, and Edmund K. Burke
    Transportation Science, 2018. [To Appear].
    [bib] [doi

Refereed Conferences Proceedings

  • Late Acceptance Hill Climbing for Constrained Covering Arrays
    Mosab Bazargani, John H. Drake, and Edmund K. Burke
    In Proceedings of the Proceedings of the 21st European Conference on the Applications of Evolutionary Computation (EvoApplications 2018), Lecture Notes in Computer Science (LNCS), Springer. Parma, Italy, April 2018. [To Appear].
    [bib

2017

Refereed Journal Papers

  • A Methodology for Determining an Effective Subset of Heuristics in Selection Hyper-heuristics
    Jorge A. Soria-Alcaraz, Gabriela Ochoa, Marco A. Sotelo-Figeroa, and Edmund K. Burke
    European Journal of Operational Research, Volume 260, pp. 972–983, Elsevier, August 2017.
    [bib] [doi
  • The Late Acceptance Hill-Climbing Heuristic
    Edmund K Burke and Yuri Bykov
    European Journal of Operational Research, Volume 258, Number 1, pp. 70–78, Elsevier, April 2017.
    [bib] [doi
  • Breakout Local Search for the Multi-objective Gate Allocation Problem
    Una Benlic, Edmund K. Burke, and John R. Woodward
    Computers & Operations Research, Volume 78, pp. 80–93, Elsevier, February 2017.
    [bib] [doi
  • A Hybrid Breakout Local Search and Reinforcement Learning Approach to the Vertex Separator Problem
    Una Benlic, Michael G. Epitropakis, and Edmund K. Burke
    European Journal of Operational Research, Volume 261, Number 3, pp. 803–818, Elsevier, 2017.
    [bib] [doi

Refereed Conferences Proceedings

  • A Hyper-heuristic for Multi-objective Integration and Test Ordering in Google Guava
    Giovani Guizzo, Mosab Bazargani, Matheus Paixão, and John H. Drake
    In Proceedings of the 9th International Symposium on Search Based Software Engineering (SSBSE 2017), Volume 10452 of Lecture Notes in Computer Science (LNCS), pages 168–174, Springer. Paderborn, Germany, September 2017.
    [bib] [doi
  • Parameter-less Late Acceptance Hill-Climbing
    Mosab Bazargani and Fernando Lobo
    In Proceedings of the Proceedings of the 2017 on Genetic and Evolutionary Computation Conference (GECCO 2017), pages 219–226, ACM. Berlin, Germany, July 2017.
    [bib] [doi
  • Solving the Distributed Two Machine Flow-shop Scheduling Problem Using Differential Evolution
    Paul Dempster, Penghao Li, and John H. Drake
    In Proceedings of the 8th International Conference on Advances in Swarm Intelligence (ICSI 2017), Part I, Volume 10385 of Lecture Notes in Computer Science (LNCS), pages 449–457, Springer. Fukuoka, Japan, July 2017.
    [bib] [doi
  • An Iterated Local Search Framework with Adaptive Operator Selection for Nurse Rostering
    Angeliki Gretsista and Edmund K. Burke
    In Proceedings of the Proceedings of the 11th Learning and Intelligent Optimization Conference (LION 11), Lecture Notes in Computer Science (LNCS), pages 93–108, Springer. Nizhny, Russia, June 2017.
    [bib] [doi
  • A Modified Indicator-based Evolutionary Algorithm (mIBEA)
    Wenwen Li, Ender Özcan, Robert John, John H. Drake, Aneta Neumann, and Markus Wagner
    In Proceedings of the Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2017), pages 1047–1054, IEEE Press. San Sebastián, Spain, June 2017.
    [bib] [doi
  • Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants
    John H. Drake, Jerry Swan, Geoff Neumann, and Ender Özcan
    In Proceedings of the Proceedings of the 17th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2017), Volume 10197 of Lecture Notes in Computer Science (LNCS), pages 189–200, Springer. Amsterdam, The Netherlands, April 2017.
    [bib] [doi

2016

Refereed Journal Papers

  • A Self-Adaptive Multimeme Memetic Algorithm Co-evolving Utility Scores to Control Genetic Operators and their Parameter Settings
    Ender Özcan, John H. Drake, Cevriye Altıntaş, and Shahriar Asta
    Applied Soft Computing, Volume 49, pp. 81–93, Elsevier, December 2016.
    [bib] [doi
  • Toward a More Realistic, Cost-effective, and Greener Ground Movement Through Active Routing: A Multiobjective Shortest Path Approach
    Jun Chen, Michal Weiszer, Giorgio Locatelli, Stefan Ravizza, Jason A. Atkin, Paul Stewart, and Edmund K. Burke
    IEEE Transactions on Intelligent Transportation Systems, Volume 17, Number 12, pp. 3524–3540, IEEE, December 2016.
    [bib] [doi
  • An Adaptive Flex-Deluge Approach to University Exam Timetabling
    Edmund K. Burke and Yuri Bykov
    INFORMS Journal on Computing, Volume 28, Number 4, pp. 781–794, Institute for Operations Research and the Management Sciences (INFORMS), November 2016.
    [bib] [doi
  • Heuristic Search for the Coupled Runway Sequencing and Taxiway Routing Problem
    Una Benlic, Alexander EI Brownlee, and Edmund K. Burke
    Transportation Research Part C: Emerging Technologies, Volume 71, pp. 333–355, Elsevier, October 2016.
    [bib] [doi
  • A Multi-agent Based Cooperative Approach to Scheduling and Routing
    Simon Martin, Djamila Ouelhadj, Patrick Beullens, Ender Özcan, Angel A. Juan, and Edmund K. Burke
    European Journal of Operational Research, Volume 254, Issue 1, pp. 169–178, Elsevier, October 2016.
    [bib] [doi
  • A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem
    John H. Drake, Ender Özcan, and Edmund K. Burke
    Evolutionary Computation, Volume 24, Number 1, pp. 113–141, MIT Press, March 2016.
    [bib] [doi

Refereed Conferences Proceedings

  • Clustering of Maintenance Tasks for the Danish Railway System
    Shahrzad M. Pour and Una Benlic
    In Proceedings of the Intelligent Systems Design and Applications: 16th International Conference on Intelligent Systems Design and Applications (ISDA 2016), Volume 557 of Advances in Intelligent Systems and Computing (AISC), pages 791–799, Springer. Porto, Portugal, December 2016.
    [bib] [doi
  • Searching for Configurations in Clone Evaluation – A Replication Study
    Chaiyong Ragkhitwetsagul, Matheus Paixao, Manal Adham, Saheed Busari, Jens Krinke, and John H. Drake
    In Proceedings of the 8th International Symposium on Search Based Software Engineering (SSBSE 2016), Volume 9962 of Lecture Notes in Computer Science (LNCS), pages 250–256, Springer. Raleigh, NC, USA, October 2016.
    [bib] [doi
  • Multi-objective Regression Test Suite Minimisation for Mockito
    Andrew J. Turner, David R. White, and John H. Drake
    In Proceedings of the 8th International Symposium on Search Based Software Engineering (SSBSE 2016), Volume 9962 of Lecture Notes in Computer Science (LNCS), pages 244–249, Springer. Raleigh, NC, USA, October 2016.
    [bib] [doi
  • Automatically Designing More General Mutation Operators of Evolutionary Programming for Groups of Function Classes Using a Hyper-heuristic
    Libin Hong, John H. Drake, John R. Woodward, and Ender Özcan
    In Proceedings of the Proceedings of the 2016 on Genetic and Evolutionary Computation Conference (GECCO 2016), pages 725–732, ACM. Denver, Colorado, USA, July 2016.
    [bib] [doi
  • Automatic Improvement of Apache Spark Queries using Semantics-preserving Program Reduction
    Zoltan A. Kocsis, John H. Drake, Douglas Carson, and Jerry Swan
    In Proceedings of the Proceedings of the 2016 on Genetic and Evolutionary Computation Conference (Companion) (GECCO 2016), pages 1141–1146, ACM. Denver, Colorado, USA, July 2016.
    [bib] [doi

Last update on 2018-02-07