Abstract
A novel extension of an existing artificial Gene Regulatory Network model is introduced, combining the dynamic adaptive nature of this model with the generative power of grammars. The use of grammars enables the model to produce more varied phenotypes, allowing its application to a wider range of problems. The performance and generalisation ability of the model on the inverted-pendulum problem, using a range of different grammars, is compared against the existing model. © 2012 Springer-Verlag.
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CITATION STYLE
Murphy, E., Nicolau, M., Hemberg, E., O’Neill, M., & Brabazon, A. (2012). Differential gene expression with tree-adjunct grammars. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7491 LNCS, pp. 377–386). https://doi.org/10.1007/978-3-642-32937-1_38
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