Formula 1 is a highly competitive and ever-evolving sport, with teams constantly searching for ways to gain an edge over the competition. In order to meet this challenge, we propose a custom Genetic Algorithm that can simulate a race strategy given data from free practices and compute an optimal strategy for a specific circuit. The algorithm takes into account a variety of factors that can affect race performance, including weather conditions as well as tire choice, pit-stops, fuel weight, and tire wear. By simulating and computing multiple race strategies, the algorithm provides valuable insights and can help make informed strategic decisions, in order to optimize the performance on the track. The algorithm has been evaluated on both a video-game simulation and with real data on tire consumption provided by the tire manufacturer Pirelli. With the help of the race strategy engineers from Pirelli, we have been able to prove the real applicability of the proposed algorithm.

Evolutionary F1 Race Strategy / Bonomi, Andrea; Turri, Evelyn; Iacca, Giovanni. - (2023), pp. 1925-1932. (Intervento presentato al convegno GECCO '23 tenutosi a Lisbon, Portugal nel 15th-19th July) [10.1145/3583133.3596349].

Evolutionary F1 Race Strategy

Iacca, Giovanni
2023-01-01

Abstract

Formula 1 is a highly competitive and ever-evolving sport, with teams constantly searching for ways to gain an edge over the competition. In order to meet this challenge, we propose a custom Genetic Algorithm that can simulate a race strategy given data from free practices and compute an optimal strategy for a specific circuit. The algorithm takes into account a variety of factors that can affect race performance, including weather conditions as well as tire choice, pit-stops, fuel weight, and tire wear. By simulating and computing multiple race strategies, the algorithm provides valuable insights and can help make informed strategic decisions, in order to optimize the performance on the track. The algorithm has been evaluated on both a video-game simulation and with real data on tire consumption provided by the tire manufacturer Pirelli. With the help of the race strategy engineers from Pirelli, we have been able to prove the real applicability of the proposed algorithm.
2023
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
New York, NY, United States
ACM
9798400701207
Bonomi, Andrea; Turri, Evelyn; Iacca, Giovanni
Evolutionary F1 Race Strategy / Bonomi, Andrea; Turri, Evelyn; Iacca, Giovanni. - (2023), pp. 1925-1932. (Intervento presentato al convegno GECCO '23 tenutosi a Lisbon, Portugal nel 15th-19th July) [10.1145/3583133.3596349].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/384851
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