Abstract
Object detection is a key point in robotics, both in localization and robot decision making. Genetic Algorithms (GAs) have proven to work well in this type of tasks, but they usually give rise to heavy computational processes. The scope of this study is the Standard Platform category of the RoboCup soccer competition, and so real-time object detection is needed. Because of this, we constraint ourselves to the use of tiny GAs. The main problem with this type of GAs is their premature convergence to local optima. In this paper we study two different approaches to overcoming this problem: the use of population re-starts, and the use of a cellular GA instead of the standard generational one. The combination of these approaches with a clever initialisation of the population has been analyzed experimentally, and from the results we can conclude that for our problem the best choice is the use of cellular GAs.
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Martínez-Gómez, J., Gámez, J.A., García-Varea, I. (2010). Comparing Cellular and Panmictic Genetic Algorithms for Real-Time Object Detection. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12239-2_27
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DOI: https://doi.org/10.1007/978-3-642-12239-2_27
Publisher Name: Springer, Berlin, Heidelberg
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