Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
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
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Using feedback in a regulatory network computational device
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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 Regulatory Network Computational Device
Genetic Programming and Evolvable Machines
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Evolutionary Algorithms (EA) are stochastic search algorithms inspired by the principles of selection and variation posited by the theory of evolution, mimicking in a simple way those mechanisms. In particular, EAs approach differently from nature the genotype - phenotype relationship, and this view is a recurrent issue among researchers. Moreover, in spite of some performance improvements, it is a true fact that biology knowledge has advanced faster than our ability to incorporate novel biological ideas into EAs. Recently, some researchers start exploring computationally our new comprehension about the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, trying to include those mechanism in the EA. One of the first successful proposals is the Artificial Gene Regulatory (ARN) model, by Wolfgang Banzhaf. Soon after some variants of the ARN with increased capabilities were tested. In this paper, we further explore the capabilities of one of those, the Regulatory Network Computational Device, empowering it with feedback connections. The efficacy and efficiency of this alternative is tested experimentally using a typical benchmark problem for recurrent and developmental systems. In order to gain a better understanding about the reasons for the improved quality of the results, we undertake a preliminary study about the role of neutral mutations during the evolutionary process.