Genetic Programming and Evolvable Machines

, Volume 8, Issue 1, pp 61–96

Evolutionary morphogenesis for multi-cellular systems


DOI: 10.1007/s10710-006-9019-1

Cite this article as:
Roggen, D., Federici, D. & Floreano, D. Genet Program Evolvable Mach (2007) 8: 61. doi:10.1007/s10710-006-9019-1


With a gene required for each phenotypic trait, direct genetic encodings may show poor scalability to increasing phenotype length. Developmental systems may alleviate this problem by providing more efficient indirect genotype to phenotype mappings. A novel classification of multi-cellular developmental systems in evolvable hardware is introduced. It shows a category of developmental systems that up to now has rarely been explored. We argue that this category is where most of the benefits of developmental systems lie (e.g. speed, scalability, robustness, inter-cellular and environmental interactions that allow fault-tolerance or adaptivity). This article describes a very simple genetic encoding and developmental system designed for multi-cellular circuits that belongs to this category. We refer to it as the morphogenetic system. The morphogenetic system is inspired by gene expression and cellular differentiation. It focuses on low computational requirements which allows fast execution and a compact hardware implementation. The morphogenetic system shows better scalability compared to a direct genetic encoding in the evolution of structures of differentiated cells, and its dynamics provides fault-tolerance up to high fault rates. It outperforms a direct genetic encoding when evolving spiking neural networks for pattern recognition and robot navigation. The results obtained with the morphogenetic system indicate that this “minimalist” approach to developmental systems merits further study.


Evolutionary computationDevelopmental systemGenotype to phenotype mappingEvolvable hardwareNeural network

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Daniel Roggen
    • 1
    • 2
  • Diego Federici
    • 3
    • 4
  • Dario Floreano
    • 1
  1. 1.Laboratory of Intelligent SystemsÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Wearable Computing LaboratoryETH ZürichZürichSwitzerland
  3. 3.Department of Computer and Information SciencesNorwegian University of Science and TechnologyTrondheimNorway
  4. 4.Google’s Zürich European Engineering CentreZürichSwitzerland