Multi-objective evolution for Car Setup Optimization

Abstract

This paper describes the winner algorithm of the Car Setup Optimization Competition that took place in EvoStar (2010). The aim of this competition is to create an optimization algorithm to fine tune the parameters of a car in the The Open Racing Car Simulator (TORCS) video game. There were five participants of the competition plus the two algorithms presented by the organizers (that do not take part in the competition). Our algorithm is a Multi-Objective Evolutionary Algorithm (MOEA) based on the Non-Dominated Sorting Genetic Algorithm (NSGAII) adapted to the constraints of the competition, that focus its fitness function in the lap time. Our results are also compared with other evolutionary algorithms and with the results of the other competition participants.