A new kind of science
Shrinking the Genotype: L-systems for EHW?
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
Creation of Neural Networks Based on Developmental and Evolutionary Principles
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Proceedings of the European Conference on Genetic Programming
Towards Development in Evolvable Hardware
EH '02 Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware (EH'02)
The Importance of Reuse and Development in Evolvable Hardware
EH '03 Proceedings of the 2003 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
Promises and challenges of evolvable hardware
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary morphogenesis for multi-cellular systems
Genetic Programming and Evolvable Machines
Robust multi-cellular developmental design
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Phenotypic, developmental and computational resources: scaling in artificial development
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Multi-cellular Developmental System in Continuous Space Using Cell Migration
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
A three-step decomposition method for the evolutionary design of sequential logic circuits
Genetic Programming and Evolvable Machines
Open-ended on-board evolutionary robotics for robot swarms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Genotype reuse more important than genotype size in evolvability of embodied neural networks
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Unsupervised learning of echo state networks: a case study in artificial embryogeny
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
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The introduction of a genotype-phenotype map modelled on biological development can potentially improve the scalability of evolutionary algorithms. Previous work by Gordon and Bentley demonstrated that such a model can be used to evolve patterns that map to useful but small phenotypes. This paper uses the same model to generate much larger patterns covering arrays of up to 64x64 cells. The results show that the model's performance is generally comparable to similar development-based systems [12, 14], and with some measures outperforms them. Additionally the inherent biases of the model are explored, such as the need to use symmetry-breaking initial conditions which some other models do not require. This exploration yields a set of guidelines that suggest what kinds of problem the model is suited to exploring.