Generating Weekly Training Plans in the Style of a Professional Swimming Coach Using Genetic Algorithms and Random Trees
Paper i proceeding, 2022

Optimal training planning is a combination of art and sci- ence, a time-consuming task that requires expert knowledge. As such, it is often exclusively available to top tier athletes. Many athletes outside the elite do not have access or cannot afford to hire a professional coach to help them create their training plans. In this study, we investigate if it is possible to use the historical training logs of elite swimmers to con- struct detailed weekly training plans similar to how a specific professional coach would have planned. We present a software system based on machine learning and genetic algorithms for generation of detailed weekly training plans based on desired volume, intensity, training frequency, and athlete characteristics. The system schedules training sessions from a library extracted from training plans written by a professional swimming coach. Results show that the proposed system is able to generate highly accurate training plans in terms of training load, types of sessions, and structure, compared to the human coach.


Training Plan Generation

Machine Learning

Exercise Intelligence

Training Planning


Rikard Eriksson

Student vid Chalmers

Johan Nicander

Student vid Chalmers

Moa Johansson

Chalmers, Data- och informationsteknik, Formella metoder

C. Mikael Mattson

Karolinska Institutet

Silicon Valley Exercise Analytics (SVEXA)

Proceedings of the 9th International Performance Analysis Workshop and Conference & 5th IACSS Conference (Advances in Intelligent Systems and Computing 1426)

2194-5357 (ISSN) 2194-5365 (eISSN)

Vol. 1426 61-68
978-3-030-99333-7 (ISBN)

9th International Performance Analysis Workshop and Conference and 5th International Conference of Computer Science in Sports Conference (PACSS)
Wien, Austria,


Informations- och kommunikationsteknik

Hälsa och teknik


Data- och informationsvetenskap


Datavetenskap (datalogi)



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