Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolving specific network statistical properties using a gene regulatory network model
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolving genes to balance a pole
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Using feedback in a regulatory network computational device
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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
Applying genetic regulatory networks to index trading
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Differential gene expression with tree-adjunct grammars
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Extracting key gene regulatory dynamics for the direct control of mechanical systems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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In recent years, our biologic understanding was increased with the comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development, and some researchers advocate the need to explore computationally this new understanding. One of the outcomes was the Artificial Gene Regulatory (ARN) model, first proposed by Wolfgang Banzhaf. In this paper, we use this model as representation for a computational device and introduce new variation operators, showing experimentally that it is effective in solving a set of benchmark problems.