SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Genetic Synthesis of Modular Neural Networks
Proceedings of the 5th International Conference on Genetic Algorithms
A Taxonomy for artificial embryogeny
Artificial Life
Bias and scalability in evolutionary development
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolving modular genetic regulatory networks
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Robust multi-cellular developmental design
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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
Why are evolved developing organisms also fault-tolerant?
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
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This paper introduces a novel multi-cellular developmental system where cells are placed in a continuous space. Cells communicate by diffusing and perceiving substances in the environment and are able to migrate around following affinities with substance gradients. The optimization process is performed using Echo State neural networks on the problem of minimizing tile size variations in the context of a tiling problem. Experimental results show that problem complexity only impacts the number of substances used, rather than the number of cells, which implies some sort of scalability with regards to the size of the phenotype. Symmetry breaking and robustness are addressed by adding noise as an intrinsic property of the model. A (positive) side effect is that the resulting model produces very robust solutions with efficient self-healing behavior in the presence of perturbations never met before.