emergent/swarm/evolutionary systems etc

[Peter MacKenzie]

Hello, I'm Peter, and I'm very much a newbie.

New in the sense of being a novice at everything to do with programming,
rather than just new to Python, so please be very explicit in your replies
(should you deem me worth of such attention).  I've long had a passing
interest in programming from a macro-conceptual level, but only very
recently have I tried to learn the details of how to impliment my ideas in
code.  Since I'm so inexperienced and learning soley from on-line
documentation in my sparse spare time, I'm finding the learning curve rather
steep, and would appreciate it if you didn't assume that I 'know' anything
(especially with formating issues).

What instigated my uptake of programming was a recently sparked, and rapidly
growing interest in emergent processes, and particularly of computer
applications for them.  I've not covered more than 20 hours of good study on
the subject (accounting for liberal procrastination), but I like to jump in
the deep end with things, and the logic of emergence really 'clicks' with my
brain.  I have, however covered biological evolution in considerably more
depth, so I'm already quite familiar with the concepts involved.

What I've been trying to do with Python is to set up something resembling
John Von Neumann's conception of an imperfectly self-replicating program:

(3.2.1 Self-Reproduction
Nearly all of the practical implementations of articial worlds to be
described are related
in some way to the seminal work of John von Neumann. In the late 1940s and
early
1950s, he devoted considerable time to the question of how complicated
machines could
evolve from simple machines.9 Specically, he wished to develop a formal
description of
a system that could support self-reproducing machines which were robust in
the sense
that they could withstand some types of mutation and pass these mutations on
to their
ospring. Such machines could therefore participate in a process of
evolution.
Inspired by Alan Turing's earlier work on universal computing machines
[Turing 36],
von Neumann devised an architecture which could full these requirements.
The ma-
chine he envisaged was composed of three subcomponents [von Neumann 66]:
1. A general constructive machine, A, which could read a description (X) of
an-
other machine, X, and build a copy of X from this description:
A + (X) ; X (3.1)
(where + indicates a single machine composed of the components to the left
and
right suitably arranged, and ; indicates a process of construction.)
2. A general copying machine, B, which could copy the instruction tape:
B + (X) ; (X) (3.2)
9 Von Neumann had diculties in dening precisely what the term
`complicated' meant. He said \I
am not thinking about how involved the object is, but how involved its
purposive operations are.
In this sense, an object is of the highest degree of complexity if it can do
very dicult and involved
things." [von Neumann 66].
3.2. PREVIOUS WORK 47
3. A control machine, C, which, when combined with A and B, would rst
activate
B, then A, then link X to (X) and cut them loose from (A + B + C):
A + B + C + (X) ; X + (X) (3.3)
Now, if we choose X to be (A + B + C), then the end result is:
A + B + C + (A + B + C) ; A + B + C + (A + B + C) (3.4)
This complete machine plus tape, [A + B + C + (A + B + C)], is therefore
self-
reproducing. From the point of view of the evolvability of this
architecture, the crucial
feature is that we can add the description of an arbitrary additional
automaton D to
the input tape. This gives us:
A + B + C + (A + B + C + D) ; A + B + C +D + (A + B + C + D) (3.5)
Furthermore, notice that if the input tape (A + B + C +D) is mutated in
such a
way that the description of automaton D is changed, but that of A, B and C
are
unaected|that is, the mutated tape is (A + B + C +D0)|then the result of
the
construction will be:
A + B + C+ D + (A + B + C + D) mutation
; A + B + C + D0 + (A + B + C+ D0)
(3.6)
The reproductive capability of the architecture is therefore robust to some
mutations
(specically, those mutations which only aect the description of D), so the
machines
are able to evolve. Von Neumann pointed out that it was the action of the
general copy-
ing automaton, B, which gave his architecture the capacity for evolving
machines of
increased complexity, because B is able to copy the description of any
machine, no mat-
ter how complicated [von Neumann 66] (p.121). This ability is clearly
demonstrated in
Equation 3.5 above.

>From Artificial Evolution to Artificial Life
Timothy John Taylor
Ph.D
University of Edinburgh
1999)

-The formatting on the equations is a bit messed up, but I hope you get the
idea.

Although the concept is quite straight forward, and I have my own ideas
about what specific features I'd like to include, I'm usure as to how to
implement it at the level of coding.  Basically, I wish to create:
1.  An entirely human-designed evolutionary engine.
2.  A file that it '1' will acess, modify, test against a goal to retrive a
score, then save with the modifications intact and ready for the next run or
loop-cycle.
3.  A buffer that will store a population of these files.
4.  A death engine that will remove low scoring files (probably as a part of
'1')

These concepts aren't new, and are already well publicised.  Variants of
them are used for determining coefficient values for engeneering, medical
etc research, but I wish to create a program that's more open ended than a
number-finder.  The Neumann model is incomplete, since it not only fails to
specify a method for mutation beyond the vague decription given, but also
fails to account for the vanishingly small chance that completely random
mutation of a program would produce any intelligible results relative to the
processor's capacity for run cycles (overlooked, perhaps, because of an
over-riding focus on making the evolutionary engine robust to mutation).  To
solve this, I propose an 'incubator' approach, where the engine would, as
part of its evolutionary toolkit, and particularly at the beginnng of the
process before the code becomes complex enough to allow the likelyhood of
non-fatal mutations, scavange code from external sources.

Regardless of any issues of copyright etc, this has the advantage of basing
the now-less random mutations on code that is known to be operable, rather
than the infinite monkies approach of random alphanumeric generation.  To
incorporate this and other revisions and refinements that I've just spotted,
the program structure would look like so:

An entirely human-designed evolutionary engine, which:
1.  Allows the user to set a goal.
2.  Takes random sections of code from a library.
3.  Places them in a text file.
4.  Applies a 'scatter shot' of random alphanumeric changes (with either a
random or a user specified % of alpahnumerals changed)
5.  Runs the file.
6.  Scores the file on how many valid lines it has, if it fufilled the goal,
how long it took, etc.
7.  Saves the score to the file (in the title?).
8.  Repeats for a user specified number of times.
9.  Selects the best programs, deletes the rest
10.  Returns to '3' and repeats until stopped.

At the moment, I'm not even sure if python can create, chop and change
files, so that the effects of a program being run can persist after it's
closed down.  The library modules are still largely incomprehensible to me,
both in their function and their implementation.  I'm a very long way from
understanding how to apply the randomised aspects of the file changes, or
much else for that matter, and I can already see a bunch of things that
would need a lot of tweaking, even if the structure is sound (this is the
first proper program I've planned, which I'm sure means that the structure
is almost certaintly gibberish).

Other things that would be handy:

A bit to make sensible sections of code less vunerable to mutation, and
concentrate changes in 'junk' areas.

Something to decrease or prevent the addition of scavanged code when the
proportion of scavanged code to random alphanumeric changes and working
code, or vice versa (with working code forming the fulcrum of the dynamic).

A 'skills' counter/protector, that would allow files to retain redundant
code if their goal was changed, rather than necessarily losing it to the
pressure of clock cycles and the equivalent of genetic drift.  (For this,
the 'bastion' module looks like it might be useful, though I'm not terribly
sure about how it works, or 'exactly' what it does)

An random goal changer, to provide an impetus for constant evolution without
the need for user input. (There's only so good you can be at any one thing)

An option to give the files an internal copy of the evolution engine, which
could then be modified by itself as it alters the entire file.  (This may
prove difficult, as although this would allow the files to develop more
efficient strategies for the process of evolution itself, it would make them
more vunerable to fatal mutations.  A possible solution would be to have a
'nuclear membrane' - a guard to prevent mutations that are particularly
likely to be fatal eg. mutations that alter significant chunks of the
engine.  It would also be possible to have multiple engines in each cell,
which could self-assess and compare scores, replacing engines that
underperform through damage or 'under-evolution' (radiation resistant
bacteria use a similar technique)


==================
All of this seems like a rather tall order, especially as I'm still
struggling with some of the things from the beginners guide.  Although I
should probably buy a book and work my way through lots of examples of
increasing complexity, I like to tackle things head-on and learn as I go.
Even just writing this message has helped me to clarify to myself what it is
I want to do.

If anybody has even bothered to read this far, I'd ask you please to give
some thought to these ideas (of myself and others), and perhaps to give me
some pointers in the right direction.  Thankyou.