Hyper-heuristics applied to class and exam timetabling problems

被引:15
|
作者
Ross, P [1 ]
Marín-Bláuquez, JG [1 ]
Hart, E [1 ]
机构
[1] Napier Univ, Sch Comp, Edinburgh EH10 5DT, Midlothian, Scotland
关键词
D O I
10.1109/CEC.2004.1331099
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Combinatorial optimisation algorithms can be both slow and fragile. That is, the quality of results produced can vary considerably with the problem and with the parameters chosen and the user must hope or the best or search for problem-specific good parameters. The idea of hyper-heuristics is to search for a good, fast, deterministic algorithm built from easily-understood heuristics that shows good performance across a range of problems. In this paper we show how the idea can be applied to class and exam timetabling problems and report results on non-trivial problems. Unlike many optimisation algorithms, the generated algorithm does not involve and solution-improving search step, it is purely constructive.
引用
收藏
页码:1691 / 1698
页数:8
相关论文
共 50 条
  • [21] Hyper-heuristics: A survey and taxonomy
    Dokeroglu, Tansel
    Kucukyilmaz, Tayfun
    Talbi, El-Ghazali
    COMPUTERS & INDUSTRIAL ENGINEERING, 2024, 187
  • [22] Generative Hyper-heuristics Tutorial
    Tauritz, Daniel R.
    Woodward, John
    PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 2022, : 1111 - 1140
  • [23] Generative Hyper-heuristics Tutorial
    Tauritz, Daniel R.
    Woodward, John
    PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2023 COMPANION, 2023, : 1069 - 1098
  • [24] Lifelong Learning Selection Hyper-heuristics for Constraint Satisfaction Problems
    Carlos Ortiz-Bayliss, Jose
    Terashima-Marin, Hugo
    Enrique Conant-Pablos, Santiago
    ADVANCES IN ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, MICAI 2015, PT I, 2015, 9413 : 190 - 201
  • [25] Examination Timetabling Automation and Optimization using Greedy-Simulated Annealing Hyper-heuristics Algorithm
    Kusumawardani, Dian
    Muklason, Ahmad
    Supoyo, Vicha Azthanty
    PROCEEDINGS OF 2019 12TH INTERNATIONAL CONFERENCE ON INFORMATION & COMMUNICATION TECHNOLOGY AND SYSTEM (ICTS), 2019, : 164 - 169
  • [26] Experimentation on Iterated Local Search Hyper-heuristics for Combinatorial Optimization Problems
    Adubi, Stephen A.
    Oladipupo, Olufunke O.
    Olugbara, Oludayo O.
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (05) : 948 - 960
  • [27] A generality analysis of multiobjective hyper-heuristics
    Li, Wenwen
    Ozcan, Ender
    Drake, John H.
    Maashi, Mashael
    INFORMATION SCIENCES, 2023, 627 : 34 - 51
  • [28] Hyper-heuristics for personnel scheduling domains
    Kletzander, Lucas
    Musliu, Nysret
    ARTIFICIAL INTELLIGENCE, 2024, 334
  • [29] Comparing Hyper-heuristics with Blackboard Systems
    Graham, Kevin
    Smith, Leslie
    PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCO'17 COMPANION), 2017, : 1141 - 1145
  • [30] The Importance of the Learning Conditions in Hyper-Heuristics
    Loureno, Nuno
    Pereira, Francisco B.
    Costa, Ernesto
    GECCO'13: PROCEEDINGS OF THE 2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2013, : 1525 - 1532