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Information transformation by virus-evolutionary genetic programming

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Abstract

This paper deals with genetic programming (GP) for information translation. The GP can generate a structured computer program, but it is difficult to define recursive functions automatically. Therefore, we propose a virus-evolutionary genetic programming (VE-GP) composed of two populations; host and virus. Here, a virus plays the role of an automatically defined function. First, the VE-GP is applied to a function approximation problem, and the simulation result shows that the VE-GP can generate a function to approximate the given function with small errors. Next, the VE-GP is applied to the information transformation for a classification task, and the simulation result shows that the VE-GP can generate a function to classify a given data set.

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Correspondence to Naoyuki Kubota.

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Kubota, N., Kojima, F., Hashimoto, S. et al. Information transformation by virus-evolutionary genetic programming. Artif Life Robotics 4, 171–174 (2000). https://doi.org/10.1007/BF02481340

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  • DOI: https://doi.org/10.1007/BF02481340

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