Evolution In Materio : Evolving Logic Gates in Liquid Crystal

Abstract

Intrinsic evolution has been shown to be capable of exploit- ing the physical properties of materials to solve problems, however most researchers have chosen to limit themselves to using standard electronic components. However, these components are human designed and inten- tionally have predictable responses, so they may not be the most suitable physical devices to use when using a stochastic search technique. In- deed allowing computer controlled evolution (CCE) to manipulate novel physical media might allow much greater scope for the discovery of un- conventional solutions. Last year the authors demonstrated, for the first time, that CCE could manipulate liquid crystal to perform computa- tional tasks (i.e frequency discrimination, robot control). In this paper, we demonstrate that it is also possible to evolve logic gates in liquid crystal.

Full text

GECCO 2004, Tutorial 1
1
Evolvable Physical Media
(or Evolution in materio)
Julian Francis Miller
Department of Electronics
The University of York, UK
http://www.evolutioninmaterio.com
jfm@ohm.york.ac.uk
2
Evolution in materio
• The manipulation of a physical system by
computer controlled evolution (CCE) of its
physical properties
• Does this mean optimisation? NO!
– It means the discovery of physical properties
that can be utilized to help solve the imposed
task
• I think that we might be able to use CCE to
invent a new technology!

GECCO 2004, Tutorial 2
3
Why we should be interested in
evolution in materio?
• Natural evolution is par excellence an algorithm
that exploits the physical properties of materials
• Artificial Evolution may be more effective when
the configurable medium has a rich and complex
physics
4
The price of programmability
• In conventional design the vast majority of
interactions that could possibly contribute to
the problem are deliberately excluded
(Michael Conrad 1988)

GECCO 2004, Tutorial 3
5
What are evolvable physical
media?
• Systems whose physical properties can be
affected by changes to controllable physical
variables
• To be evolvable the media must be
– able to be reset
– strong genetic inheritance of physical
characteristics
6
A tricky question
• What kinds of physical systems are
most easily exploited by an artificial
intrinsic evolutionary process?

GECCO 2004, Tutorial 4
7
How do you evolve matter to
compute? (view 1)
Configuration data
Incident signal
Modified signal
Test for
desired
response
Configuration population subject to artificial
evolution
Fitness
calculation
8
The Field Programable Matter
Array (View 2)
wires
Chemical
substrate
A single piece of
material?
Region to which
voltage may be
applied
KEY REQUIREMENT
Removing the voltage
must cause the material
to relax to its
former state

GECCO 2004, Tutorial 5
9
Has anybody demonstrated
evolvable physical media?
• Gordon Pask - Ferrous sulphate
• Adrian Thompson - silicon
• Adrian Stoica, Didier Keymeulen, Riccardo
Zebulum - silicon
• Huelsbergen, Rietman and Slous - silicon
• Derek Linden - reed switch array
• Paul Layzell and Jon Bird - silicon
• Simon Harding and Julian Miller -liquid crystal
10
Gordon Pask
“Physical analogues to the
Growth of a Concept”
Mechanization of Thought
Processes, Symposium 10,
National Physical Laboratory,
H.M.S.O (London) pp 765-
794, 1958.

GECCO 2004, Tutorial 6
11
Some background
• “We believe that if the ‘complexity barrier’ is to
be broken, a major revolution in production and
programming techniques is required…We may as
well start that with the notion that with 10
10
parts
per cubic foot there will be no circuit diagram
possible…We would manufacture ‘logic by the
pound’, using techniques more like a bakery than
of an electronics factory” [see Cariani 1993].
• People were looking for self-wiring, self-
organising machines way back then!
12
What Pask was trying to do
• Build a machine without any explicit definition of
its parts (self-building)
• Able to build its own “relevance criteria” and find
the observables required to solve the task
• The device would choose its own training set and
the type of training variables
• He needed a physically rich machine which could
be adaptively steered

GECCO 2004, Tutorial 7
13
Schematic of Electrode array and
Ferrous sulphate medium
14
What Pask did
“…the rewarding procedure acts by supplying more
current for constructing threads whenever the
mode of problem solution, implied by the
existence of a certain thread structure, satisfies an
external criterion, such as maximising the output
of the process. In this learning by reward
procedure some threads flourish, others will prove
abortive. It is a lengthy and inefficient kind of
learning not unlike natural selection” - (Pask 58)

GECCO 2004, Tutorial 8
15
“We have made an ear…”
• “We have made an ear and a magnetic receptor.
The ear can discriminate two frequencies, one of
the order of fifty cycles per second and the other
on the order of one hundred cycles per second.
The ‘training procedure’ takes approximately half
a day and once having got the ability to recognize
sound at all, the ability to recognize and
discriminate two sounds comes more rapidly…” -
(see Cariani 1993)
16
View of actual apparatus

GECCO 2004, Tutorial 9
17
Modern generic evolvable
platform
18
Generic in silico
evolvable
patform

GECCO 2004, Tutorial 10
19
The Xilinx 6216 Field
Programmable Gate Array (FPGA)
Function unit
20
Adrian Thompson’s experiment

GECCO 2004, Tutorial 11
21
What were the active parts of the
circuit?
22
Logic circuit representation of
evolved circuit
Numbers in hexagons
are estimates of nanosec
delays
Assumption: FPGA cells acting
as Boolean logic gates

GECCO 2004, Tutorial 12
23
Analysis of circuit
• When input is 1, parts A and B settle to a constant
logic state (in 20ns) until next input goes to 0 (part
B settles to 1). This selects the Mux in part C own
output as input (so oscillates but settles to a logic
value). So circuit inactive until end of pulse.
24
Further analysis
• Thompson and Layzell checked that there were no
circulating glitches (short duration pulses) during
static phase
• PSPICE simulation (with extensive variation of
gate delays and parasitic capacitance) did not
produce real circuit behaviour
• Build CMOS Mux version of circuit didn’t
reproduce actual circuit behaviour.
• Time delays on connection from A to B&C crucial
to evolved circuit behaviour

GECCO 2004, Tutorial 13
25
Conclusion
• Core of timing mechanism is a subtle
property of the VLSI medium
• They ruled out:
– glitches, beat frequencies
– metastability
– thermal time-constants (self-heating)
• Evolution has exploited properties of the
system that are at present unknown
26
Evolution of antennas
• Physical evolution of antennas using reed
switches
• evolution of wire segment antennas in
simulation.

GECCO 2004, Tutorial 14
27
Toplogies of Reed Switches
28
Evolve and test apparatus

GECCO 2004, Tutorial 15
29
Test set-up
30
Results

GECCO 2004, Tutorial 16
31
Evolution of Astable
Multivibrators in Silico
• Huelsbergen, Rietman and Slous
• Also used the Xilinx 6216
32
Paul Layzell: Evolvable
Motherboard

GECCO 2004, Tutorial 17
33
Evolution of inverter fitness
34
An evolved inverter that used the
measurement apparatus as a
circuit component!

GECCO 2004, Tutorial 18
35
Evolving an Oscillator
• Fitness function chosen to reward high-amplitude
signals present at output
• Huelsbergen et al. sampled through an a/d
converter and were troubled by aliasing errors.
• Layzell used a frequency to voltage converter to
avoid aliasing errors
• target frequency was 25KHz
36
Evolving an oscillator

GECCO 2004, Tutorial 19
37
Evolved Oscillator: fitness
function
• a and f represent output amplitude and
frequency average over 20 samples, f
min
and f
max
are the maximum and minimum of 20 frequencies
sampled.
38
Evolved circuit and output response
In 20 runs 10 were successful to within 1% with a minimum
amplitude of 100mV

GECCO 2004, Tutorial 20
39
Analysis of evolved oscillators
• Difficult to clarify how the circuits work
• If transistors are replaced by nominally identical
ones, the output frequency can change by up to
30%
• Simulation of circuits with parasitic capacitance
failed to oscillate
• Some oscillators only worked while a nearby
soldering iron was switched on!
• Programmable switches’ characteristics are almost
certainly important for circuit operation
40
An evolved radio
• Some circuits that achieved high fitness
were found to be amplifying radio signals
(generated by nearby PCs) that were stable
enough over the sampling period to give
good fitness scores
• The circuit board tracks were being used as
an aerial!

GECCO 2004, Tutorial 21
41
Evolution in silico or in materio?
• Unconstrained evolution in silicon is possible
• Intrinsic evolution often utilizes incidental
environmental effects to achieve a solution
• Although these can be a nuisance we should not
give up. It is too early to worry about analysis.
• Other material systems may have advantages. At
the very least evolution may tell us that
computational circuits can be constructed in
unusual systems. This may inspire conventional
design in such systems (i.e. evolution as a
discovery tool)
42
Should Evolvable Matter be on
the “edge of chaos”
• Langton observed that interesting computation in
cellular automata occurs on the “edge of chaos”.
This suggests a good place to look in materials.
• Supramolecular systems
• Mesoscale systems (see “The Middle Way” in refs)
• IDEA: Use a genotype to define a physical order in
a resetable material where chaos removes the order

GECCO 2004, Tutorial 22
43
What material systems should we
use?
• Liquid crystal
• Conducting and electroactive polymers
• Voltage controlled colloids
• Irradiated Silicon
• Langmuir-Blodgett films
• nanoparticle suspensions
• microbial consortia
44
Growing wires in nanoparticle
suspensions
• "
Dielectrophoretic Assembly of Electrically
Functional Microwires from Nanoparticle
Suspensions" Science Vol 294 November 2001

GECCO 2004, Tutorial 23
45
Liquid crystal programmable
matter?
• Mesoscopic organisation
Smectic nematic
Organic elongated Polar
molecules
Many other types
46
Twisted nematic LC Display

GECCO 2004, Tutorial 24
47
Types and uses of liquid crystal
• Dye doped
• polymer dispersed
• discotic
• in plane systems
48
Evolution in Liquid Crystal
• This year Simon Harding and myself
carried out evolution in a novel medium:
liquid crystal
• We have evolved a number of functions
using a Liquid Crystal Display
• The rest of the tutorial is about that work

GECCO 2004, Tutorial 25
49
Experimental setup
50
In materio evolution in progress

GECCO 2004, Tutorial 26
51
Inputs and Outputs to LCD
8 connectors:
Ground (fixed)
input signal
(fixed)
output signal
(fixed)
5 voltages (-10v
- 10v)
52
Genetic representation
• Genotype in two parts
• First part
– 64 integers in range 0-8:
• 64 contacts on the LCD that can be connected to any
of eight points (or not connected - left to float)
• constrained: only one contact can connect to the
incident applied signal and one to the output pickup
• Second part
– 5, 16 bit integers that represents voltages -10v
to +10v

GECCO 2004, Tutorial 27
53
Genetic Algorithm
• Population 40
• Top 5 genotypes promoted. Population
filled with tournament selected (size 5)
others that were mutated (5 mutations each)
• 100 generations.
• It took approximately 1 minute to evalute
each generation
54
Task: Tone discriminator
• Evolve a “circuit” that can discriminate between
two possible applied signals:
• Signals were square waves, 0-5V, 100Hz, 5kHz*
• Test sequence:
• 250ms 5Khz, 250ms 100Hz, 250ms 5KHz (i.e.
1250 pulses 5KHz, 25 pulses 100 Hz, 1250 pulses)
• Reward: count percentage output < 0.1V for
100Hz and output >0.1V for 5kHz
• * Many pairs of frequencies have now been
tried and proved successful

GECCO 2004, Tutorial 28
55
Best evolved “circuit” response
56
Analysis (preliminary)
• Crosspoint switches unlikely to be involved as
they are designed for high frequency audio/video
signals. The feedthrough capacitance is 0.2pF and
the switch capacitance is 20pF.
• Our current hypothesis is that the LCD is acting as
a configurable RC network but there is much more
work to be done to confirm/deny this
• When an evolved configuration is reloaded it fails
to work, however if a population contains that
individual it evolves to work in 2-3 generations

GECCO 2004, Tutorial 29
57
Further work
• Increase density of connections to Liquid crystal
display
• Increase number of applied voltages
• Robot control
• Use different types of LC
• Attempt to evolve solutions to much harder
problems
• Understand how the devices work
• Examine potential of other physical systems
58
Conclusions
• To be able to evolve things you need richness
• It is time to let evolution to create technology for
us. Not tell it what technology it must use.
• We know it has already created the technology of
living things.
• I would like to see the growth of “evolution in
materio” as a research community

GECCO 2004, Tutorial 30
59
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