Skip to main content
SpringerLink
Log in

Frequency Fitness Assignment on JSSP: A Critical Review

  • Conference paper
  • First Online:
Applications of Evolutionary Computation (EvoApplications 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13989))

  • 725 Accesses

Abstract

Metaheuristic navigation towards rare objective values instead of good objective values: is it a good idea? We will discuss the closed and open ends after presenting a successful replication study of Weise et al.’s ‘frequency fitness assignment’ for a hillClimber on the job shop scheduling problem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Extracted from personal communication with Manuel López-Ibañez and Luís Paquete, quoted with permission.

  2. 2.

    An individual’s objective value and its fitness are not necessarily the same thing, especially in self-normalizing algorithms [19, 25, 31]. In that respect, the name “Frequency Fitness Assignment” is rather debatable, but history would have it this way, so we shall stick with it.

  3. 3.

    and independently replicated!.

References

  1. Achary, T., Pillay, A., Jembere, E.J.: Towards rigorous foundations for metaheuristic research. In: IJCCI, pp. 151–157 (2022)

    Google Scholar 

  2. Adams, J., Balas, E., Zawack, D.: The shifting bottleneck procedure for job shop scheduling. Manage. sci. 34(3), 391–401 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  3. Applegate, D., Cook, W.: A computational study of the job-shop scheduling problem. ORSA J. Comput. 3(2), 149–156 (1991)

    Article  MATH  Google Scholar 

  4. Bäck, T., Fogel, D.B., Michalewicz, Z.: Handb. Evol. Comput. Release 97(1), B1 (1997)

    Google Scholar 

  5. Begley, C.G., Ellis, L.M.: Raise standards for preclinical cancer research. Nature 483(7391), 531–533 (2012)

    Article  Google Scholar 

  6. Blum, C., Sampels, M.: An ant colony optimization algorithm for shop scheduling problems. J. Mathe. Model. Algorithms 3(3), 285–308 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  7. Boese, K.D., Kahng, A.B.: Best-so-far vs. where-you-are: Implications for optimal finite-time annealing. Syst. Control lett. 22(1), 71–78 (1994)

    Google Scholar 

  8. Braam, F., van den Berg, D.: Which rectangle sets have perfect packings? Oper. Res. Perspecti., p. 100211 (2022)

    Google Scholar 

  9. Brouwer, N., Dijkzeul, D., Koppenhol, L., Pijning, I., Van den Berg, D.: Survivor selection in a crossoverless evolutionary algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1631–1639 (2022)

    Google Scholar 

  10. Brucker, P., Jurisch, B., Sievers, B.: A branch and bound algorithm for the job-shop scheduling problem. Discrete Applied Mathematics 49(1-3), 107–127 (Mar 1994). https://doi.org/10.1016/0166-218x(94)90204-6. https://doi.org/10.1016/0166-218x(94)90204-6

  11. de Bruin, E.: Repostory containing source material for this study (2023). https://github.com/egedebruin/FFAProject

  12. Camacho Villalón, C.L., Stützle, T., Dorigo, M.: Grey wolf, firefly and bat algorithms: three widespread algorithms that do not contain any novelty. In: Dorigo, M., et al. (eds.) ANTS 2020. LNCS, vol. 12421, pp. 121–133. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60376-2_10

    Chapter  Google Scholar 

  13. Castelli, M., Manzoni, L., Mariot, L., Nobile, M.S., Tangherloni, A.: Salp swarm optimization: a critical review. Expert Syst. Appl. 189, 116029 (2022)

    Article  Google Scholar 

  14. Chakraborty, S., Bhowmik, S.: An efficient approach to job shop scheduling problem using simulated annealing. Int. J. Hybrid Inf. Technol. 8(11), 273–284 (2015)

    Google Scholar 

  15. Cheeseman, P.C., Kanefsky, B., Taylor, W.M., et al.: Where the really hard problems are. In: IJCAI. vol. 91, pp. 331–337 (1991)

    Google Scholar 

  16. Chen, B., Potts, C.N., Woeginger, G.J.: A review of machine scheduling: Complexity, algorithms and approximability. Handb. Comb. Optim., pp. 1493–1641 (1998)

    Google Scholar 

  17. Cockburn, A., Dragicevic, P., Besançon, L., Gutwin, C.: Threats of a replication crisis in empirical computer science. Commun. ACM 63(8), 70–79 (2020)

    Article  Google Scholar 

  18. Dahmani, R., Boogmans, S., Meijs, A., Van den Berg, D.: Paintings-from-polygons: simulated annealing. In: International Conference on Computational Creativity (ICCC 2020) (2020)

    Google Scholar 

  19. De Jonge, M., Van den Berg, D.: Plant Propagation Parameterization: Offspring & Population Size, Evo* LBA’s 2020, vol. 2, pp. 1–4. Springer (2020)

    Google Scholar 

  20. Demirkol, E., Mehta, S., Uzsoy, R.: Benchmarks for shop scheduling problems. Eur. J. Oper. Res. 109(1), 137–141 (1998)

    Article  MATH  Google Scholar 

  21. Dijkzeul, D., Brouwer, N., Pijning, I., Koppenhol, L., van den Berg, D.: Painting with Evolutionary Algorithms. In: Martins, T., Rodríguez-Fernández, N., Rebelo, S.M. (eds.) EvoMUSART 2022. LNCS, vol. 13221, pp. 52–67. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-03789-4_4

    Chapter  Google Scholar 

  22. Eiben, A.E., Smith, J.E., et al.: Introduction to evolutionary computing, vol. 53. Springer (2003) DOIurl10.1007/978-3-662-05094-1

    Google Scholar 

  23. Fisher, H., Thompson, G.: Probabilistic learning combination of local job-shop scheduling rules, prentice-hall, englewood cliffs. Ind. Sched., pp. 225–251 (1963)

    Google Scholar 

  24. Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Mathe. Oper. Res. 1(2), 117–129 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  25. Geleijn, R., van der Meer, M., van der Post, Q., van den Berg, D., et al.: The plant propagation algorithm on timetables: First results. EVO LBA’s, p. 2 (2019)

    Google Scholar 

  26. Gromicho, J.A., van Hoorn, J.J., da Gama, F.S., Timmer, G.T.: Solving the job-shop scheduling problem optimally by dynamic programming. Comput. Oper. Res. 39(12), 2968–2977 (2012)

    Google Scholar 

  27. van Horn, G., Olij, R., Sleegers, J., van den Berg, D.: A predictive data analytic for the hardness of hamiltonian cycle problem instances. Data Analytics 2018, 101 (2018)

    Google Scholar 

  28. Huang, K.L., Liao, C.J.: Ant colony optimization combined with taboo search for the job shop scheduling problem. Comput. Oper. Res. 35(4), 1030–1046 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  29. Hutter, M., Legg, S.: Fitness uniform optimization. IEEE Trans. Evol. Comput. 10(5), 568–589 (2006)

    Article  Google Scholar 

  30. Jain, A.S., Meeran, S.: Deterministic job-shop scheduling: Past, present and future. Euro. J. Oper. Res. 113(2), 390–434 (1999)

    Article  MATH  Google Scholar 

  31. de Jonge, M., van den Berg, D.: Parameter Sensitivity Patterns in the Plant Propagation Algorithm. No. April 2020, IJCCI 2020: Proceedings of the 12th International Joint Conference on Computational Intelligence (2020). https://doi.org/10.5220/0010134300920099

  32. Koppenhol, L., Brouwer, N., Dijkzeul, D., Pijning, I., Sleegers, J., Van Den Berg, D.: Exactly characterizable parameter settings in a crossoverless evolutionary algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1640–1649 (2022)

    Google Scholar 

  33. Lawler, E.L., Lenstra, J.K., Kan, A.H.R., Shmoys, D.B.: Sequencing and scheduling: Algorithms and complexity. Handb. Oper. Res. Manage. Sci. 4, 445–522 (1993)

    Google Scholar 

  34. Lawrence, S.: Resouce constrained project scheduling: An experimental investigation of heuristic scheduling techniques (supplement). Carnegie-Mellon University, Graduate School of Industrial Administration (1984)

    Google Scholar 

  35. López-Ibáñez, M., Branke, J., Paquete, L.: Reproducibility in evolutionary computation. ACM Trans. Evol. Learn. Optimization 1(4), 1–21 (2021)

    Article  Google Scholar 

  36. Maxwell, S.E., Lau, M.Y., Howard, G.S.: Is psychology suffering from a replication crisis? what does failure to replicate really mean? American Psychologist 70(6), 487 (2015)

    Google Scholar 

  37. Niewenhuis, D., van den Berg, D.: Making hard(ER) bechmark test functions. In: IJCCI, pp. 29–38 (2022)

    Google Scholar 

  38. Silver, L.M.: An introduction to Fanny Sossisj’ optimization algorithm. IIIE Transactions on Algorithmic Archaeology Iridescent Pegasus Publishers. Edinborough, Scotland 16(85), 1976–1993, (2023)

    Google Scholar 

  39. Sleegers, J., van den Berg, D.: Looking for the hardest hamiltonian cycle problem instances. In: IJCCI, pp. 40–48 (2020)

    Google Scholar 

  40. Sleegers, J., van den Berg, D.: The hardest hamiltonian cycle problem instances: the plateau of yes and the cliff of no. SCSC (2022)

    Google Scholar 

  41. Sleegers, J., Olij, R., van Horn, G., van den Berg, D.: Where the really hard problems aren’t. Oper. Res. Perspect. 7, 100160 (2020)

    MathSciNet  Google Scholar 

  42. Sleegers, J., Thomson, S.L., Van den Berg, D.: Making hard (ER) bechmark test functions, pp. 105–111 (2022)

    Google Scholar 

  43. Sörensen, K., Arnold, F., Palhazi Cuervo, D.: A critical analysis of the improved clarke and wright savings algorithm. Int. Trans. Oper. Rese. 26(1), 54–63 (2019)

    Google Scholar 

  44. Storer, R.H., Wu, S.D., Vaccari, R.: New search spaces for sequencing problems with application to job shop scheduling. Manage. sci. 38(10), 1495–1509 (1992)

    Article  MATH  Google Scholar 

  45. Taillard, E.: Benchmarks for basic scheduling problems. Euro. J. Oper. Res. 64(2), 278–285 (1993)

    Google Scholar 

  46. The Open Science Collaboration: Estimating the reproducibility of psychological science. Science 349(6251), aac4716 (2015)

    Google Scholar 

  47. Van Den Berg, D., Adriaans, P.: Subset sum and the distribution of information. In: Proceedings of the 13th International Joint Conference on Computational Intelligence, pp. 135–141 (2021)

    Google Scholar 

  48. Van Laarhoven, P.J., Aarts, E.H., Lenstra, J.K.: Job shop scheduling by simulated annealing. Oper. Res. 40(1), 113–125 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  49. Vrielink, W., van den Berg, D.: Fireworks algorithm versus plant propagation algorithm. In: IJCCI. pp. 101–112 (2019)

    Google Scholar 

  50. Vrielink, W., Van den Berg, D.: A dynamic parameter for the plant propagation algorithm. Evo* LBA’s pp. 5–9 (2021)

    Google Scholar 

  51. Vrielink, W., Van den Berg, D.: Parameter control for the Plant Propagation Algorithm Parameter control for the Plant Propagation Algorithm, pp. 1–4. No. March, Evo LBA’s 2021, Springer (2021)

    Google Scholar 

  52. Weise, T., Li, X., Chen, Y., Wu, Z.: Solving job shop scheduling problems without using a bias for good solutions. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM (Jul 2021). https://doi.org/10.1145/3449726.3463124. https://doi.org/10.1145/3449726.3463124

  53. Yamada, T., Nakano, R.: A genetic algorithm applicable to large-scale job-shop problems. In: PPSN. vol. 2, pp. 281–290 (1992)

    Google Scholar 

Download references

Acknowledgements

The reviewers of EvoSTAR2023 did an excellent job on reviewing this paper, especially Reviewer 3. I definitely hope to meet you in person, because there’s probably a lot to learn from you still. Also, thanks to Manuel López-Ibáñez, and Luís Paquete, for discussions and letting me use their quote.

Author information

Authors and Affiliations

  1. Master Artificial Intelligence, Informatics Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

    Ege de Bruin & Daan van den Berg

  2. Department of Computing Science and Mathematics, University of Stirling, Stirling, UK

    Sarah L. Thomson

  3. Universiteit van Amsterdam, Amsterdam, Netherlands

    Daan van den Berg

Authors
  1. Ege de Bruin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarah L. Thomson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daan van den Berg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daan van den Berg .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    João Correia

  2. University of York, York, UK

    Stephen Smith

  3. Al Hussein Technical University, Amman, Jordan

    Raneem Qaddoura

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

de Bruin, E., Thomson, S.L., Berg, D.v.d. (2023). Frequency Fitness Assignment on JSSP: A Critical Review. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-30229-9_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30228-2

  • Online ISBN: 978-3-031-30229-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation