Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Evolving recursive functions for the even-parity problem using genetic programming
Advances in genetic programming
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolving recursive programs by using adaptive grammar based genetic programming
Genetic Programming and Evolvable Machines
Evolutionary morphogenesis for multi-cellular systems
Genetic Programming and Evolvable Machines
Evolving modular genetic regulatory networks
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Self Modifying Cartesian Genetic Programming: Fibonacci, Squares, Regression and Summing
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Evolving specific network statistical properties using a gene regulatory network model
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Developments in Cartesian Genetic Programming: self-modifying CGP
Genetic Programming and Evolvable Machines
ReNCoDe: a regulatory network computational device
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Evolving genes to balance a pole
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
The squares problem and a neutrality analysis with ReNCoDe
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
The Regulatory Network Computational Device
Genetic Programming and Evolvable Machines
GEARNet: grammatical evolution with artificial regulatory networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The relationship between the genotype and the phenotype in Evolutionary Algorithms (EA) is a recurrent issue among researchers. Based on our current understanding of the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, some researchers start exploring computationally this new insight, including those mechanism in the EA. The Artificial Gene Regulatory (ARN) model, proposed by Wolfgang Banzhaf was one of the first tentatives. Following his seminal work some variants were proposed with increased capabilities. In this paper, we present another modification of this model, consisting in the use the regulatory network as a computational device where feedback edges are used. Using two classical benchmarks, the n-bit parity and the Fibonacci sequence problems, we show experimentally the effectiveness of the proposal.