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
themorphogenetic system. The results obtained with
themorphogenetic system indicate that this 'minimalist'
approach to developmental systems merits further
study.
%0 Journal Article
%1 Roggen:2007:GPEM
%A Roggen, Daniel
%A Federici, Diego
%A Floreano, Dario
%D 2007
%J Genetic Programming and Evolvable Machines
%K Developmental Evolutionary Genotype Neural algorithms, computation, evolvable genetic hardware, mapping, network phenotype system, to
%N 1
%P 61--96
%R doi:10.1007/s10710-006-9019-1
%T Evolutionary morphogenesis for multi-cellular
systems
%V 8
%X 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
themorphogenetic system. The results obtained with
themorphogenetic system indicate that this 'minimalist'
approach to developmental systems merits further
study.
@article{Roggen:2007:GPEM,
abstract = {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
themorphogenetic system. The results obtained with
themorphogenetic system indicate that this 'minimalist'
approach to developmental systems merits further
study.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Roggen, Daniel and Federici, Diego and Floreano, Dario},
biburl = {https://www.bibsonomy.org/bibtex/2baf0968f188239b4a5410b7f7d511f54/brazovayeye},
doi = {doi:10.1007/s10710-006-9019-1},
interhash = {17e136ab3cacc765ce56005f8e2f483e},
intrahash = {baf0968f188239b4a5410b7f7d511f54},
issn = {1389-2576},
journal = {Genetic Programming and Evolvable Machines},
keywords = {Developmental Evolutionary Genotype Neural algorithms, computation, evolvable genetic hardware, mapping, network phenotype system, to},
month = {March},
number = 1,
pages = {61--96},
size = {36 pages},
timestamp = {2008-06-19T17:50:23.000+0200},
title = {Evolutionary morphogenesis for multi-cellular
systems},
volume = 8,
year = 2007
}