Using feedback in a regulatory network computational device

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.

[1]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[2]  Man Wong,et al.  Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming , 2005, Genetic Programming and Evolvable Machines.

[3]  E. Davidson.  The Regulatory Genome: Gene Regulatory Networks In Development And Evolution , 2006 .

[4]  Julian Francis Miller,et al.  Developments in Cartesian Genetic Programming: self-modifying CGP , 2010, Genetic Programming and Evolvable Machines.

[5]  Kwong-Sak Leung,et al.  Evolving recursive functions for the even-parity problem using genetic programming , 1996 .

[6]  Marc Schoenauer,et al.  Evolving specific network statistical properties using a gene regulatory network model , 2009, GECCO.

[7]  Peter Eggenberger,et al.  Evolving Morphologies of Simulated 3d Organisms Based on Differential Gene Expression , 1997 .

[8]  Julian Francis Miller,et al.  Self Modifying Cartesian Genetic Programming: Fibonacci, Squares, Regression and Summing , 2009, EuroGP.

[9]  W. Banzhaf,et al.  Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence. , 2006, Bio Systems.

[10]  Wolfgang Banzhaf,et al.  Evolving Dynamics in an Artificial Regulatory Network Model , 2004, PPSN.

[11]  Dario Floreano,et al.  Evolutionary morphogenesis for multi-cellular systems , 2007, Genetic Programming and Evolvable Machines.

[12]  Marc Schoenauer,et al.  Evolving Genes to Balance a Pole , 2010, EuroGP.

[13]  Ernesto Costa,et al.  ReNCoDe: A Regulatory Network Computational Device , 2011, EuroGP.

[14]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[15]  Josh Bongard,et al.  Evolving modular genetic regulatory networks , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[16]  John R. Koza,et al.  Genetic programming 2 - automatic discovery of reusable programs , 1994, Complex Adaptive Systems.

[17]  Peter Eggenberger-Hotz.  Evolving Morphologies of Simulated 3d Organisms Based on Differential Gene Expression , 2007 .

[18]  W. Banzhaf.  Artificial Regulatory Networks and Genetic Programming , 2003 .

[19]  Una-May O'Reilly,et al.  Genetic Programming II: Automatic Discovery of Reusable Programs. , 1994, Artificial Life.

[20]  Julian F. Miller,et al.  A scalable solution to n-bit parity via artificial development , 2009, 2009 Ph.D. Research in Microelectronics and Electronics.

[21]  S. Teichmann,et al.  Gene regulatory network growth by duplication , 2004, Nature Genetics.

[22]  Cândida Ferreira,et al.  Genetic Representation and Genetic neutrality in gene Expression Programming , 2002, Adv. Complex Syst..