Abstract
In this paper we present a novel general framework for encoding and evolving networks called Common Genetic Encoding (CGE) that can be applied to both direct and indirect encoding methods. The encoding has important properties that makes it suitable for evolving neural networks: (1) It is complete in that it is able to represent all types of valid phenotype networks. (2) It is closed, i. e. every valid genotype represents a valid phenotype. Similarly, the encoding is closed under genetic operators such as structural mutation and crossover that act upon the genotype. Moreover, the encoding’s genotype can be seen as a composition of several subgenomes, which makes it to inherently support the evolution of modular networks in both direct and indirect encoding cases. To demonstrate our encoding, we present an experiment where direct encoding is used to learn the dynamic model of a two-link arm robot. We also provide an illustration of how the indirect-encoding features of CGE can be used in the area of artificial embryogeny.
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Kassahun, Y., Metzen, J.H., de Gea, J., Edgington, M., Kirchner, F. (2007). A General Framework for Encoding and Evolving Neural Networks. In: Hertzberg, J., Beetz, M., Englert, R. (eds) KI 2007: Advances in Artificial Intelligence. KI 2007. Lecture Notes in Computer Science(), vol 4667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74565-5_17
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DOI: https://doi.org/10.1007/978-3-540-74565-5_17
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