Transcript Eric Duchon.

Evolution and
Complex
Structures:
Simulated Evolution Hints
at Features?
Eric Duchon
March 17, 2008
Complex Structures
Darwin:
To suppose that the eye with all its inimitable
contrivances for adjusting the focus to different
distances, for admitting different amounts of light, and for
the correction of spherical and chromatic aberration,
could have been formed by natural
selection, seems, I freely confess, absurd in
the highest degree.
Even today, it is not clear how
many of the complex structures
in Nature evolved.
The Eye
How do genetic mutations
create more complex eyes
without intermediate steps
destroying their advantages?
Arguments For Simulation
• Fossil records not complete enough to track
emergence of complexity
• Lab experiments limited by number of generations
and by ability to track mutations through generations
• Computer simulations allows exact tracking of
mutations
• Limited by computer resources and a simplified
model
Computer Models
Evolutionary simulations are usually modified cellular
automata. Although not useful for directly modeling
biological systems, they can offer support for
suspicions and theories. In particular, work with Avida
has elucidated how complexity can arise.
Digital Organisms
• The genome is a circular sequence of instructions (26
possible)
• Energy: received single instruction processing units
(SIPs) relative to the rest of the organisms
• Rate of errors when replicating the genome
– 0.175: an instruction to be copied is switched for another
– 0.05: single instruction is deleted or added
• Environment determined by what merited additional
SIPs
Competition and Fitness
• Competition was introduced by assigning additional
computational time to organisms which demonstrated
logical functions
• The SIPs an organism received was proportional to
the product of genome length and computational
merit.
Reading a Digital Genome
Locating Complexity
• Computational merit was assigned on the basis of
complexity of the genome required to produce the
logic function.
• With the possible instructions, NOT and NAND were
the easiest to create while EQU was the most difficult
(it required at least 19 instructions). So to investigate
complexity, the emergence of the EQU operation was
tracked.
Case Study: A genotype with all
operations
• This genotype achieved all
logical operations. Not all
the mutations were
advantageous, as seen on
top right. However, even the
deleterious mutation that
knocked out the NAND
function was essential for
forming EQU in the next
replication.
Conclusions
• Support for Darwin’s general idea that
complex structures evolve from simpler
ones.
• A reasonable demonstration of the
usefulness of cellular automata?
More Generally,
• Out of 50 populations, 23 gained EQU.
• The final genomes ranged from 49 to 356
instructions, so tendency to larger genomes.
• Median of seven of eight simpler functions
already apparent before EQU.
• The mutation to EQU caused 20 of 23
genotypes to lose at least one simpler
operation.
• But when only EQU was rewarded, no
populations evolved that trait.