Abstract
Genetic algorithms is a technique widely used to evolve controllers of agents or robots in dynamic environments. In this paper we describe a modification to a single-robot-based evolution of a controller – a distributed parallel genetic algorithm where the pool of chromosomes is dispersed over a multi-robot society. Robots share their experience in solving the task by direct exchange of individually evolved successful strategies coded by chromosomes.
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Kubík, A. (2003). Distributed Genetic Algorithm: Learning by Direct Exchange of Chromosomes. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds) Advances in Artificial Life. ECAL 2003. Lecture Notes in Computer Science(), vol 2801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39432-7_37
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DOI: https://doi.org/10.1007/978-3-540-39432-7_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20057-4
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