Shrinking the Genotype: L-systems for EHW?
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
Implementing Asynchronous Embryonic Circuits using AARDVArc
EH '02 Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware (EH'02)
Generative representations for evolutionary design automation
Generative representations for evolutionary design automation
Genetic Programming and Evolvable Machines
Development Brings Scalability to Hardware Evolution
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Evolving modular genetic regulatory networks
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Developments in Cartesian Genetic Programming: self-modifying CGP
Genetic Programming and Evolvable Machines
Fractal gene regulatory networks for control of nonlinear systems
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
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
GEARNet: grammatical evolution with artificial regulatory networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Computational development traditionally focuses on the use of an iterative, generative mapping process from genotype to phenotype in order to obtain complex phenotypes which comprise regularity, repetition and module reuse. This work examines whether an evolutionary computational developmental algorithm is capable of producing a phenotype with no known pattern at all: the irrational number PI. The paper summarizes the fractal protein algorithm, provides a new analysis of how fractals are exploited by the developmental process, then presents experiments, results and analysis showing that evolution is capable of producing an approximate algorithm for PI that goes beyond the limits of precision of the data types used.