Developments on the Regulatory Network Computational Device

Evolutionary Algorithms (EA) approach differently from nature the genotype-phenotype relationship, a view that is a recurrent issue among researchers. Recently, some researchers have started exploring computationally the new comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, by trying to include those mechanisms in the EAs. One of the first successful proposals was the Artificial Regulatory Network (ARN) model. Soon after some variants of the ARN, including different improvements over the base model, were tested. In this paper, the authors revisit the Regulatory Network Computational Device (ReNCoDe), now empowered with feedback connections, providing a formal demonstration of the typical solutions evolved with this representation. The authors also present some preliminary results of using a variant of the model to deal with problems with multiple outputs.

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