Abstract
In this work we design a genetic representation and its genetic operators to encode individuals for evolving Dynamic System Models in a Qualitative Differential Equation form, for System Identification. The representation proposed, can be implemented in almost every programming language without the need of complex data structures, this representation gives us the possibility to encode an individual whose phenotype is a Qualitative Differential Equation in QSIM representation. The Evolutionary Computation paradigm we propose for evolving structures like those found in the QSIM representation, is a variation of Genetic Programming called Gene Expression Programming. Our proposal represents an important variation in the multi-gene chromosome structure of Gene Expression Programming at the level of the gene codification structure. This gives us an efficient way of evolving QSIM Qualitative Differential Equations and the basis of an Evolutionary Computation approach to Qualitative System Identification.
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Serrato Paniagua, R., Flores Romero, J.J., Coello Coello, C.A. (2007). A Genetic Representation for Dynamic System Qualitative Models on Genetic Programming: A Gene Expression Programming Approach. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_4
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DOI: https://doi.org/10.1007/978-3-540-76631-5_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76630-8
Online ISBN: 978-3-540-76631-5
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