Abstract
This paper presents results of a study of a genetic algorithm, designed to evolve cellular automata for solving a given problem. Evolution is performed between the cell’s update-rules in a local manner, allowing for easy parallelization. As a case study, the algorithm was applied to the density classification problem: classifying any given initial configuration according to the percentage of 1-valued cells. The main result presented in this paper is an ’unlearning’ phenomenon: highly fit solutions are generated by the algorithm, only to be ’unlearned’ and completely disappear as the evolutionary run continues.
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Leskes, B., Sloot, P.M.A. (2004). Unlearning Phenomena in Co-evolution of Non-uniform Cellular Automata. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds) Cellular Automata. ACRI 2004. Lecture Notes in Computer Science, vol 3305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30479-1_18
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DOI: https://doi.org/10.1007/978-3-540-30479-1_18
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