Evolving
'portrait painter programs' using Genetic Programming (Darwinian evolution)
and a portrait of Darwin.
(this
is a work in progress - excuse the crude site and text ---- open your
browser page this wide) --------------------------------------------------------|
 
Source (fitness function image) and an ordered progression
of the best of the 33 days (so far) but best is less interesting than
most creative family members ..
This is a conceptual piece that creates and evolves a related
family of abstract portrait painters. Each Portrait below is created via
one evolved computer program. These programs are created and evolve by
Darwinian evolutionary techniques (crossover, mutation, survival) using
the automatic programming technique called Genetic Programming (see tech
and credit details at the bottom of this page). The environment they prosper
and have offspring in (or not) is the resemblance fitness function of
the most famous Portrait painting of Darwin (1st image above). The 'most
fit' (resemble the portrait better than their neighbors) of a population
are 'married' together to create 'more successful' offspring. The genes
(or function set) were not specifically created for the portrait painting
task, but were our genes from what we evolved from any different -- surely
not originally created for writing computer programs or making art. But
evolve they must.
Can you bring the ghost (creativity) out of the machine using the ghost
of Darwin (his namesake techniques and portrait). This is a work in progress
piece on things human (the creativity of art, the way life evolved) and
things creatively computer - and in the process evolving portraits that
are alive (they are not just images but programs with evolved painting
strategies that can be reused and married together).
Here is a sample of the last 33 days of evolution (
RUN on 1 PC). The portraits below are in order, starting with
the first population and moving in chronological order to the present.
I have culled my favorite portraits. These are but a few of the 1000s
that slowly evolve towards the Darwin portrait below. With regular Genetic
Programming (GP) the end result (“optimization”) is what is
important. With this process it is more about the journey ("creativity"),
while the overall population gets better at Darwin's
resemblance, that is less the point (the
Darwin goal is simply the creative spark or more specifically the environment).
Since I save the genes of each portrait, I can combine (marry) and re-evolve
any in new variants, see HERE . I have both
picked portraits that were the best at resemblance as well as "strange
uncles" who while less strong at the main goal as thier dominant
relative, are still artistically compelling (most below are creative "strange
uncles" - as are most artists). Notice as you browse, how as in real
evolution, certain strategies remain in the gene pool, eventually combining
and mutating with other strategies
into new combined strategies. I intersperse the source
image of Darwin throughout, so you can compare. Remember the goal is not
to recreate the Darwin portrait but to use it (and Darwinian techniques)
to evolve abstract portrait painters from the computer primordial ooze.
These then are an alive, related family tree (100, 000s generations) of
portrait painting programs. - Steve DiPaola (evolve@dipaola.org)
- comments? and requests for interbreeding like HERE.
Feb
1 We start evolving portraits -- after a few populations --
we get the color -- with a simple ramped split and then curves ..

At
100s in, mid tones mingle, then a first strategy appears: bands can resemble
the strong vertical lighting of Darwin portrait (4,5 below)
this
vertical band strategy holds for many runs, but eventually twisting and
manipulating the bands ensues ..

this
twisting and curving of the initial vertical bands continues, but 2 below
the bands thin, allowing more subtleties

Feb.
02 - 20,000 populations in , then combined with imbedded strategies
of curves and twists - till this last (5) 'face glow' forms

this
glowing face hi light (5 above) is now the dominant strategy; slowly the
glow/banding/curves evolve to the first 'heads' shapes
 
colors
and forms erupt, as the quicker early evolution slows down, making slower
progress but going through a very colorful stage

Feb.
04 we stay caught on this evolutionary plateau, repeating themes
and beginning to use "genetic drift" to slowly find a new niche
...
an interesting irony (which I exploit), is when the system drifts and
loses it way (from resemblance) is when I think it is most creative
note how, like real living organisms, the programs reuses and intermingles
it best genes (strategies)
-- note the repeated themes:
the
stage that began with the 1st image below and went wandering, ended up
at the next major dominant strategy - the 2nd image

Feb.
9th: this 2nd image above, with its strong hilight and gradation
to skin color, took over the niche, heralding in the soft blobby age
this
soft blobby 3d form flourishes with many sub strategies, the right eye
above (1) doesn't hold right away but does resurface later
a new
group of themes emerge, as we slowly progress and evolve, note the reoccurring
forms
Feb
12th: A new addition strategy forms, the soft blob form first
picks a dark patch (1 below) and it evolves into the eye area (2, 3, 4,
5)
this
"left raccoon eye patch" becomes dominant and combines with
other repeated themes, including the return of the right eye
skin
texture which evolved in images above, initially above the eye patch,
spreads all over ( skin texture? )
stuck
in another plateau, all themes dance around, but mostly the texture +
negative (& positive) space of the right eye and left eye patch
Feb
18th hardly evolving, the wondering begins and I let them play
with texture by increase image complexity (size of allowed program which
makes for slower runs because of the additional genes)
 
Feb
20th Evolving away towards Darwin, 20 days and still going. While
they are still not at a realistic resemblance, they do have an abstract
center head shape, remember many I have picked are not the best of a run
(best resemblance at that time) just ones that show the creative flow
and strategies.. The two below are the current best fitness but again
the journey is more important the result - these are programs with genes
of evolved visual strategies - we always had a final Darin - what we're
after is to evolve painters from computer ooze.

Feb 27 - then a more painterly era starting evolving,
this first image below, in some form, being the most evolved but with
many variants ...
march 5th - below 1st is the current best - showing a
primitive beard,skin and eye shadow colors (compared to old best, 1st
3 above)

Note
how, even late in the run, even though all painter programs are related,
you can still get very a-typical painters, the 2nd and 4th above are examples,
diversity can still make for some strange bedfellows.
Still
runnung - more updates soon. -- dipaola.org - sdipaola@sfu.ca
Some Tech and Reference details
I
am using Genetic Programming written in Java, the program is based on
Laurence Ashmore's Master thesis, as well
as his supervisor Miller who pioneered Cartesian GP that allows for genetic
drift and is well suited for visual work. This work is using Ashmore's
Java code, I have only slightly modified it for this series. Unlike most
creative GP systems where a user is the fitness function, this systems
uses a resemblance algorithm as fitness, so it runs automatically. I am
able to pick interesting individuals during the runs and save both their
genetic code (so I can reuse it or combine them later - HERE
- or render higher res images) and the thumbnails here. These images
use a more painterly Hue, Saturation and Value (HSV) color space as well
as a function set that works well for images. For additional info on the
GP work, check back here (it is still running now) and see Ashmore's site:
www.emoware.org and Miller's site: www.cs.bham.ac.uk/~jfm/ For a good
overview of GP see http://www.genetic-programming.com
I
am grateful to Ashmore for his work and support.
All
images are un-retouched and at high compression and low resolution.?
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