Skip to main content
SpringerLink
Log in

Intelligence Planning for Aerobic Training Using a Genetic Algorithm

  • Conference paper
  • First Online:
Advances in Natural Language Processing, Intelligent Informatics and Smart Technology (SNLP 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 684))

Included in the following conference series:

Abstract

A training plan is an important part of aerobic training. A training plan with a good sequence of high intensive training sessions and low intensive training sessions will substantially raise athletic performance. A creation of training plan require a sport scientist or a sports coach to do. An athlete who trains with limit in sports science knowledge may get injury. In this study, we propose a systemic implementation using a genetic algorithm (GA) to find optimal training plan. Comparison of this study result and an independently created, apparently reliable training plan, it reveal that GA is obtain capability to find optimal training plan.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Banister, E.W.: Modeling elite athletic performance. Physiol. Test. Elite Athl. 403–424 (1991)

    Google Scholar 

  2. Busso, T., Denis, C., Bonnefoy, R., Geyssant, A., Lacour, J.-R.: Modeling of adaptations to physical training by using a recursive least squares algorithm. J. Appl. Physiol. 82, 1685–1693 (1997)

    Article  Google Scholar 

  3. Goldberg, D., Holland, J.: Genetic algorithms and machine learning. Mach. Learn. 3, 95–99 (1988)

    Article  Google Scholar 

  4. Meng, F.-W., Chen, K.-C., Lin, K.-C., Chen, R.-C.: Scheduling volleyball games using linear programming and genetic algorithm. Inf. Technol. J. 13, 2411 (2014)

    Article  Google Scholar 

  5. Atan, T., Hüseyinoǧlu, O.P.: Simultaneous scheduling of football games and referees using Turkish league data. Int. Trans. Oper. Res. (2015)

    Google Scholar 

  6. Zhang, R., Ong, S.K., Nee, A.Y.C.: A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling. Appl. Soft Comput. 37, 521–532 (2015)

    Article  Google Scholar 

  7. Rahman, H.F., Sarker, R., Essam, D.: A genetic algorithm for permutation flow shop scheduling under make to stock production system. Comput. Ind. Eng. 90, 12–24 (2015)

    Article  Google Scholar 

  8. Mohtashami, A.: A novel dynamic genetic algorithm-based method for vehicle scheduling in cross docking systems with frequent unloading operation. Comput. Ind. Eng. 90, 221–240 (2015)

    Article  Google Scholar 

  9. Pattanayak, P., Kumar, P.: A computationally efficient genetic algorithm for MIMO broadcast scheduling. Appl. Soft Comput. 37, 545–553 (2015)

    Article  Google Scholar 

  10. Ew, B., Tw, C.: Planning for future performance: implications for long term training. Can. J. Appl. Sport Sci. 5, 170–176 (1980)

    Google Scholar 

  11. Allen, H., Coggan, A.: Training and Racing with a Power Meter. VeloPress (2010)

    Google Scholar 

  12. Fortin, F.-A., Rainville, D., Gardner, M.-A.G., Parizeau, M., Gagné, C.: DEAP: Evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)

    MathSciNet  MATH  Google Scholar 

  13. Droettboom, M., Hunter, J., Firing, E., Caswell, T.A., Elson, P., Dale, D., Lee, J.-J., McDougall, D., Root, B., Straw, A., Seppänen, J.K., Nielsen, J.H., May, R., Varoquaux, G., Yu, T.S., Moad, C., Gohlke, C., Würtz, P., Hisch, T., Silvester, S., Ivanov, P., Whitaker, J., Cimarron, W., Hobson, P., Giuca, M., Thomas, I., Mmetz-bn, E.J., Evans, J., Hyams, D., Nemec, N.: Matplotlib: v1.4.3. (2015)

    Google Scholar 

  14. Introduction to the Foundation Plan for Intermediate/Advanced Riders. https://www.britishcycling.org.uk/knowledge/article/izn20140929-Training-Introduction-to-the-Foundation-Plan-for-Intermediate—Advanced-0

  15. Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming. Genetic Algorithms. Oxford University Press, Oxford, UK (1996)

    MATH  Google Scholar 

  16. Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley (2005)

    Google Scholar 

Download references

Acknowledgements

Many thanks to Mr. Roy Morien of the Naresuan University Language Centre for his editing assistance and advice on English expression in this document.

Author information

Authors and Affiliations

  1. Research Center for Academic Excellence in Nonlinear Analysis and Optimization, Faculty of Science, Naresuan University, Phitsanulok, Thailand

    Kreangsak Tamee

  2. Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Phitsanulok, Thailand

    Nattapon Kumyaito & Kreangsak Tamee

Authors
  1. Nattapon Kumyaito
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kreangsak Tamee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kreangsak Tamee .

Editor information

Editors and Affiliations

  1. School of Information, Computer, and Communication Technology (ICT), Sirindhorn International Institute of Technology (SIIT), Thammasat University, Khlong Luang, Pathum Thani, Thailand

    Thanaruk Theeramunkong

  2. Faculty of Science and Technology, Department of Computer Science, Thammasat University, Khlong Luang, Pathum Thani, Thailand

    Rachada Kongkachandra

  3. Language and Semantic Technology Laboratory, National Electronics and Computer Technology Center, Khlong Luang, Pathum Thani, Thailand

    Thepchai Supnithi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kumyaito, N., Tamee, K. (2018). Intelligence Planning for Aerobic Training Using a Genetic Algorithm. In: Theeramunkong, T., Kongkachandra, R., Supnithi, T. (eds) Advances in Natural Language Processing, Intelligent Informatics and Smart Technology. SNLP 2016. Advances in Intelligent Systems and Computing, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-319-70016-8_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70016-8_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70015-1

  • Online ISBN: 978-3-319-70016-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation