Abstract I describe the Utrecht Machine (UM), a
discrete artificial regulatory network designed for
studying how evolution discovers biochemical
computation mechanisms. The corresponding binary genome
format is compatible with gene deletion, duplication,
and recombination. In the simulation presented here, an
agent consisting of two UMs, a sender and a receiver,
must encode, transmit, and decode a binary word over
time using the narrow communication channel between
them. This communication problem has chicken-and-egg
structure in that a sending mechanism is useless
without a corresponding receiving mechanism. An
in-depth case study reveals that a coincidence creates
a minimal partial solution, from which a sequence of
partial sending and receiving mechanisms evolve. Gene
duplications contribute by enlarging the regulatory
network. Analysis of 60,000 sample runs under a variety
of parameter settings confirms that crossover
accelerates evolution, that stronger selection tends to
find clumsier solutions and finds them more slowly, and
that there is implicit selection for robust mechanisms
and genomes at the codon level. Typical solutions
associate each input bit with an activation speed and
combine them almost additively. The parents of
breakthrough organisms sometimes have lower fitness
scores than others in the population, indicating that
populations can cross valleys in the fitness landscape
via outlying members. The simulation exhibits back
mutations and population-level memory effects not
accounted for in traditional population genetics
models. All together, these phenomena suggest that new
evolutionary models are needed that incorporate
regulatory network structure.
%0 Journal Article
%1 mitchener-evolution-communication-protocols-2014
%A Mitchener, W. Garrett
%D 2014
%I MIT Press - Journals
%J Artificial Life
%K alife evolution_of_communication
%N 4
%P 491--530
%R 10.1162/artl_a_00146
%T Evolution of Communication Protocols Using an
Artificial Regulatory Network
%U http://dx.doi.org/10.1162/ARTL_a_00146
%V 20
%X Abstract I describe the Utrecht Machine (UM), a
discrete artificial regulatory network designed for
studying how evolution discovers biochemical
computation mechanisms. The corresponding binary genome
format is compatible with gene deletion, duplication,
and recombination. In the simulation presented here, an
agent consisting of two UMs, a sender and a receiver,
must encode, transmit, and decode a binary word over
time using the narrow communication channel between
them. This communication problem has chicken-and-egg
structure in that a sending mechanism is useless
without a corresponding receiving mechanism. An
in-depth case study reveals that a coincidence creates
a minimal partial solution, from which a sequence of
partial sending and receiving mechanisms evolve. Gene
duplications contribute by enlarging the regulatory
network. Analysis of 60,000 sample runs under a variety
of parameter settings confirms that crossover
accelerates evolution, that stronger selection tends to
find clumsier solutions and finds them more slowly, and
that there is implicit selection for robust mechanisms
and genomes at the codon level. Typical solutions
associate each input bit with an activation speed and
combine them almost additively. The parents of
breakthrough organisms sometimes have lower fitness
scores than others in the population, indicating that
populations can cross valleys in the fitness landscape
via outlying members. The simulation exhibits back
mutations and population-level memory effects not
accounted for in traditional population genetics
models. All together, these phenomena suggest that new
evolutionary models are needed that incorporate
regulatory network structure.
@article{mitchener-evolution-communication-protocols-2014,
abstract = {Abstract I describe the Utrecht Machine (UM), a
discrete artificial regulatory network designed for
studying how evolution discovers biochemical
computation mechanisms. The corresponding binary genome
format is compatible with gene deletion, duplication,
and recombination. In the simulation presented here, an
agent consisting of two UMs, a sender and a receiver,
must encode, transmit, and decode a binary word over
time using the narrow communication channel between
them. This communication problem has chicken-and-egg
structure in that a sending mechanism is useless
without a corresponding receiving mechanism. An
in-depth case study reveals that a coincidence creates
a minimal partial solution, from which a sequence of
partial sending and receiving mechanisms evolve. Gene
duplications contribute by enlarging the regulatory
network. Analysis of 60,000 sample runs under a variety
of parameter settings confirms that crossover
accelerates evolution, that stronger selection tends to
find clumsier solutions and finds them more slowly, and
that there is implicit selection for robust mechanisms
and genomes at the codon level. Typical solutions
associate each input bit with an activation speed and
combine them almost additively. The parents of
breakthrough organisms sometimes have lower fitness
scores than others in the population, indicating that
populations can cross valleys in the fitness landscape
via outlying members. The simulation exhibits back
mutations and population-level memory effects not
accounted for in traditional population genetics
models. All together, these phenomena suggest that new
evolutionary models are needed that incorporate
regulatory network structure.},
added-at = {2015-02-02T12:07:21.000+0100},
author = {Mitchener, W. Garrett},
biburl = {https://www.bibsonomy.org/bibtex/24028e82413a0bd8c746750d6478b33ae/mhwombat},
doi = {10.1162/artl_a_00146},
interhash = {b69afa9da588de450f5a2c950d677f34},
intrahash = {4028e82413a0bd8c746750d6478b33ae},
journal = {Artificial Life},
keywords = {alife evolution_of_communication},
month = oct,
number = 4,
pages = {491--530},
publisher = {{MIT} Press - Journals},
timestamp = {2016-07-12T19:25:30.000+0200},
title = {Evolution of Communication Protocols Using an
Artificial Regulatory Network},
url = {http://dx.doi.org/10.1162/ARTL_a_00146},
volume = 20,
year = 2014
}