Talk I gave at Ben Gurion University of the Negev in Israel on the 24rd/June/2009. These are a series of talks for the period in which I visited BGU as a distinguished visiting scientist
General Principles of Intellectual Property: Concepts of Intellectual Proper...
Evolutionary Algorithms for Self-Organising Systems
1. An Evolutionary Algorithm Approach
to Guiding the Evolution of
Self-Organised Systems
Natalio Krasnogor
Interdisciplinary Optimisation Laboratory
Automated Scheduling, Optimisation & Planning Research Group
School of Computer Science
Centre for Integrative Systems Biology
School of Biology
Centre for Healthcare Associated Infections
Institute of Infection, Immunity & Inflammation
University of Nottingham
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
1 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
2. Previous Talk Slides At
http://www.slideshare.net/nxk
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
2 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
3. Overview
• Motivation
• Towards “Dial a Pattern” in Complex Systems
• Methodological Overview
• Virtual Complex Systems
Au • Physical Complex Systems
• Nanoparticle Simulation Details
• Results
• Conclusions & Further work
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
3 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
4. This work was done in collaboration with Prof. P. Moriarty and his group at the
School of Physics and Astronomy at the University of Nottingham
Based on the papers
P.Siepmann, C.P. Martin, I. Vancea, P.J. Moriarty, and N. Krasnogor. A genetic algorithm
approach to probing the evolution of self-organised nanostructured systems. Nano
Letters, 7(7):1985-1990, 2007. http://dx.doi.org/10.1021/nl070773m
G. Terrazas, P. Siepman, G. Kendal, and N. Krasnogor. An evolutionary methodology for
the automated design of cellular automaton-based complex systems. Journal of Cellular
Automata, 2(1):77-102, 2007. http://www.oldcitypublishing.com/JCA/JCA.html
L. Cronin, N. Krasnogor, B. G. Davis, C. Alexander, N. Robertson, J.H.G. Steinke,
S.L.M. Schroeder, A.N. Khlobystov, G. Cooper, P. Gardner, P. Siepmann, and B.
Whitaker. The imitation game—a computational chemical approach to recognizing life.
Nature Biotechnology, 24:1203-1206, 2006.
All papers available at: http://www.cs.nott.ac.uk/~nxk/publications.html
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
4 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
5. Motivation
- Automated design and optimisation of complex
systems’ target behaviour
- cellular automata/ ODEs/ P-systems models
- physically/chemically/biologically implemented
-present a methodology to tackle this problem
-supported by experimental illustration
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
5 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
6. Major advances in the rational/analytical design of large and
complex systems have been reported in the literature and more
recently the automated design and optimisation of these systems by
modern AI and Optimisation tools have been introduced.
It is unrealistic to expect every large & complex physical, chemical
or biological system to be amenable to hand-made fully analytical
designs/optimisations.
We anticipate that as the number of research challenges and
applications in these domains (and their complexity) increase we
will need to rely even more on automated design and optimisation
based on sophisticated AI & machine learning
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
6 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
7. Major advances in the rational/analytical design of large and
complex systems have been reported in the literature and more
recently the automated design and optimisation of these systems by
This has happened before in other
modern AI and Optimisation tools have been introduced.
research and industrial disciplines,e.g:
It is unrealistic to expect every large & complex physical, chemical
•VLSI design
or biological system to be amenable to hand-made fully analytical
•Space antennae design
designs/optimisations.
•Transport Network design/optimisation
•Personnel Rostering
•Scheduling and timetabling
We anticipate that as the number of research challenges and
applications in these domains (and their complexity) increase we
will need to rely even more on automated design and optimisation
based on sophisticated AI & machine learning
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
6 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
8. Major advances in the rational/analytical design of large and
complex systems have been reported in the literature and more with
That is, complex systems are plagued
NP-Hardness, non-approximability,
recently the automated design and optimisation of these systems by
modern AI and Optimisation toolsuncertainty, undecidability, etc results
This has happened before in other have been introduced.
research and industrial disciplines,e.g:
It is unrealistic to expect every large & complex physical, chemical
•VLSI design
or biological system to be amenable to hand-made fully analytical
•Space antennae design
designs/optimisations.
•Transport Network design/optimisation
•Personnel Rostering
•Scheduling and timetabling
We anticipate that as the number of research challenges and
applications in these domains (and their complexity) increase we
will need to rely even more on automated design and optimisation
based on sophisticated AI & machine learning
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
6 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
9. Major advances in the rational/analytical design of large and
complex systems have been reported in the literature and more with
That is, complex systems are plagued
NP-Hardness, non-approximability,
recently the automated design and optimisation of these systems by
modern AI and Optimisation toolsuncertainty, undecidability, etc results
This has happened before in other have been introduced.
research and industrial disciplines,e.g:
It is unrealistic to expect every large & complex physical, chemical
•VLSI design
or biological system to be amenable to hand-made fully analytical
•Space antennae design
designs/optimisations.
•Transport Network design/optimisation
•Personnel Rostering
Yet, they are routinely solved by
•Scheduling and timetabling
We anticipate that as the number of research challenges and design
sophisticated optimisation and
techniques, like evolutionary
applications in these domains (and their complexity) increase we
algorithms, machine learning, etc
will need to rely even more on automated design and optimisation
based on sophisticated AI & machine learning
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
6 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
10. Automated Design/Optimisation is not only good because it can
solve larger problems but also because this approach gives access
to different regions of the space of possible designs (examples of
this abound in the literature)
Space of all possible designs/optimisations
Automated
Analytical
Design
Design
(e.g. evolutionary)
A distinct view of the space of possible designs could
enhance the understanding of underlying system
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
7 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
11. The research challenge :
For the Engineer, Chemist, Physicist, Biologist :
To come up with a relevant (MODEL) SYSTEM M*
For the Computer Scientist:
To develop adequate sophisticated algorithms -beyond
exhaustive search- to automatically design or optimise existing
designs on M* regardless of computationally (worst-case)
unfavourable results of exact algorithms.
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
8 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
12. Towards “Dial a Pattern” in Complex Systems
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
9 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
13. Towards “Dial a Pattern” in Complex Systems
es
ctur
Stru
ical .S
Lex
C
te
re
rete
isc
D
ted
Disc
u
st rib
Di
Continuous (simulated) CS
How do we program?
Disc
rete
/Con
tin. (
phys
ical)
CS
Dis
cre
te/C
ont
inu
os (B
iolo
gic
al)
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
9 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
14. Methodological Overview
Dial a Pattern requires:
Parameter Learning/Evolution Technology
Structural Learning/Evolution Technology
Integrated Parameter/Structural Learning/Evolution Tech.
in silico or experimental implementation
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
10 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
15. Initial Attempts at a “Dial a Pattern” Methodology
behaviour CA-based / Real
emergent vs target complex system
Parameters/Structure
Evolutionary
algorithms
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
11 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
16. Embodied Evolution
Evolutionary Scheme
Some parts of it are embedded into a
physical, chemical or biological substrate.
Strong embodiment
Week embodiment
Genes Phenotypes Fitnesses
Variation & selection mechanisms
(or other metaheuristic scheme)
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
12 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
17. A Complex Mapping
Fitness(es)
Phenotypes
Genotypes
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
13 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
18. The CHELLnet: Unifying Investigation in Artificial
Cellularity and Complexity
Synthesis of abiotic life-like functionality in complex chemical systems through
open-ended evolution
The CHELLnet comprised four sub-projects, each with researchers in universities
across the UK
http://www.CHELLnet.org
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
14 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
19. Life-like functionality through evolved
complexity in 3 different platforms
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
15 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
20. What is the CHELLnet?
BrainCHELL
- directing assembly of conducting networks so that there is function
encoded in the structure of the product.
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
16 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
21. What is the CHELLnet?
VesiCHELL
- complexity and pattern formation within lipid-bounded systems
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
17 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
22. What is the CHELLnet?
WellCHELL
- model miniature laboratory system with multiple chemical flow reactors
where conditions of chemical processes computer controlled
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
18 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
23. What is the CHELLnet?
Evolvable CHELLware
wellCHELL
behaviour brainCHELL
emergent vs target
vesiCHELL
CHELL platforms
Evolutionary
algorithms parameters
Evolvable CHELLware
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
19 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
24. “we will implement an object-oriented, platform-independent,
evolutionary engine (EE). The EE will have a user-friendly interface
that will allow the various platform users (i.e. wellCHELL,
The Evolutionary Engine brainCHELL, vesiCHELL) to specify the platform with which the EE
will interact”
Evolvable CHELLware grant application
- no data types
- no evaluation module - data types and bounds
- no parameters - evaluation module (‘plug in’)
- EA or other ML parameters
specialised
generic GA results
GA
XML Evaluation
module
Java servlet
problem-specific
web-based web-based
configuration execution
module module
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
20 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
25. What is the CHELLnet?
Evolvable CHELLware
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
21 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
26. What is the CHELLnet?
Evolvable CHELLware
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
22 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
27. What is the CHELLnet?
Evolvable CHELLware
Log details
Results graph
Visual Visual
representation representation
of target of best result
if applicable
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
23 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
28. What is the CHELLnet?
Evolvable CHELLware
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
24 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
29. What is the CHELLnet?
Evolvable CHELLware
First steps towards embodied evolution on multiple, distinct platforms. This are
being developed.
We have proofs of concept working with models/simulators:
1.Proof of concept using cellular automaton-based models
2.Self-organised nanostructured systems
3. WellChell (in Manchester)
4. SPM (in Nottingham, 2 sites [CS, P&A])
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
25 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
30. Examples of Target Evolution in
Complex systems
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
26 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
31. Parameter Learning/Evolution Technology Example
- Self-organising processes
- Modelled using cellular automata, gass latice, ODEs, etc
- infinite, regular grid of cells
- each cell in one of a finite number of states
- at a given time, t, the state of a cell is a function of the states of its
neighbourhood at time t-1.
Example
- infinite sheet of graph paper
- each square is either black or white ?
- in this case, neighbours of a cell are the eight squares touching it
- for each of the 28 possible patterns, a rules table would state
whether the center cell will be black or white on the next time step.
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
27 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
32. CA continuous Turbulence Gas Lattice
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program Gas Lattice
28 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
33. CA continuous Turbulence Gas Lattice
d
ve
n
ol
ive
Ev
G
globals
[
row ;; current row we are now calculating
done? ;; flag used to allow you to press the go
button multiple times
]
patches-own
[
value ;; some real number between 0 and 1
]
to setup-general
set row screen-edge-y ;; Set the current row to be
the top
set done? false
cp ct
end
;; ]
end
……..
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program Gas Lattice
28 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
34. Structural Learning/Evolution Technology Example
Wang Tiles Models
Temperature T
Glue Strength Matrix
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
29 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
35. Structural Learning/Evolution Technology Example
Wang Tiles Models
en
iv
G
Temperature T
Glue Strength Matrix
d
ve
ol
Ev
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
29 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
36. Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
30 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
37. Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
31 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
38. Parameter Learning/Evolution Technology Example
lecA- PAO1 mvaT-
Env.
Params
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
32 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
39. Parameter Learning/Evolution Technology Example
lecA- PAO1 mvaT-
d
d
ve
ve
ol
ol
Ev
Ev
Env.
Params
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
32 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
40. How Do We Program These Complex
Systems?
behaviour Complex System
emergent vs target
How do we measure this? parameters
How similar is to ?
Evolutionary
algorithms
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
33 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
41. The Universal Similarity Metric (USM)
is a measure of similarity between two given objects in terms of
information distance:
where K(o) is the Kolmogorov complexity
Prior Kolmogorov complexity K(o): The length of
the shortest program for computing o by a Turing
machine
Conditional Kolmogorov complexity K(o1|o2):
How much (more) information is needed to produce
object o1 if one already knows object o2 (as input)
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
34 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
42. The Universal Similarity Metric (USM)
- Is the USM a good objective function for evolving target spacio-temporal
behaviour in a CA system?
- methodology for answering this question
- experimental results
Fitness Distance Correlation
GENOTYPE PHENOTYPE FITNESS
CA model USM
Clustering
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
35 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
43. Data set
For each CA system:
• Keep all but one parameter the same
• Produce 10 behaviour patterns through the variable
parameter
• Repeat for other parameters
EXAMPLE
turb_c4 refers to the spacio-temporal pattern produced by
the fourth variation in parameter c of a Turbulence CA
system
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
36 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
44. Produced by MODEL(p1,p2,…,pn)
p1 p2 pn
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
37 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
45. Clustering
• does the USM detect similarity of phenotype with a target pattern?
• if yes, it should be able to correctly cluster spatio-temporal patterns that
look similar together
• and, those similar patterns should be related to a specific family of
images arising from the variation of a single parameter
Fitness Distance Correlation
GENOTYPE PHENOTYPE FITNESS
CA model USM
• calculate a similarity matrix filled with the results Clustering
of the application of the USM to a set of objects
• during the clustering process, similar objects should be grouped together
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
38 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
46. Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
39 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
47. Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
40 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
48. Fitness Distance Correlation
• correlation analyses of a given fitness function versus parametric
(genotype) distance.
• larger numbers indicate the problem could be optimised by a GA
• numbers around zero [-0.15, 0.15] indicate bad correlation
• scatter plots are helpful Fitness Distance Correlation
GENOTYPE PHENOTYPE FITNESS
CA model USM
Target
Clustering
1 2 3
distance = 2 Fitness = USM (T,D)
Designoid
1 4 3
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
41 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
49. Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
42 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
50. The Evolutionary Engine
“we will implement an object-oriented, platform-independent, evolutionary engine
(EE). The EE will have a user-friendly interface that will allow the various platform
users to specify the platform with which the EE will interact”
Evolvable CHELLware grant application
- no data types
- no evaluation module - data types and bounds
- no parameters - evaluation module (‘plug in’)
- GA parameters
specialised
generic GA results
GA
XML Evaluation
module
Java servlet
problem-specific
web-based web-based
configuration execution
module module
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
43 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
51. A brief overview of Genetic Algorithms
Motivation
- optimisation problems global optimum
- large search space
- inspired by Darwinian evolution
- area covered?
- degree of order?
- similarity to target pattern?
22 0.25 1.0 4.5 1.05
simulator fitness function
genotype fitness
phenotype
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
44 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
52. Results on CAs
Target Designoid
e5
f3
.
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
45 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
53. Target Designoid
Target usm(F,T) e(i) e(c) e(r) E
p 0.91980 0.26843 0.35314 0.05552 0.22569
.
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
46 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
54. Dialling a Pattern in Meta-Automata
Remember the standard numbering of
rules:
Encoding of the elementary rule 145
t0
Neighbourhoods at t3
Output states at t4
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
47 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
55. A Meta-Automaton is a special class of non-
uniform automata
Its defined by a spatio-temporal lattice
The set of 256 standard rules
Special variables k-cells and t-times
The semantics is:
k consecutive cells are assigned to the same rules,
rules can be different among distinct k-groups
Every Total_Time/ t timesteps rules are reassigned to
groups
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
48 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
56. Meta-Automaton (k=2, t=2)
k=2
Group 1 Group 2
Phase 1
t=2
Phase 2
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
49 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
57. Evolving (k=1,2,t=1) Meta-Automaton
Target Designoid Target Designoid Target Designoid
T D T D
Target Designoid Target Designoid
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
50 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
58. Evolving (k=4,t=1) Meta-Automaton
G. Terrazas, P. Siepman, G. Kendal,
and N. Krasnogor. An evolutionary
methodology for the automated
design of cellular automaton-based
complex systems. Journal of Cellular
Automata, 2007
Target Designoid
Target Designoid
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
51 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
59. Self-Organised Nanostructured Systems
Thiol-passivated Au nanoparticles
Gold core
Thiol groups
Au Sulphur ‘head’
Alkane ‘tail’, e.g. octane
~3nm Dispersed in toluene, and spin cast
onto native-oxide-terminated silicon
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
52 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
60. Au nanoparticles: Morphology
AFM images taken by Matthew O. Blunt, Nottingham
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
53 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
61. Nanoparticle Simulations
Solvent is represented as a two-
dimensional lattice gas
Each lattice site represents 1nm2
Nanoparticles are square, and
occupy nine lattice sites
Based on the simulations of Rabani et al.
(Nature 2003, 426, 271-274). Includes
modifications to include next-nearest
neighbours to remove anisotropy.
http://www.nottingham.ac.uk/physics/research/nano/
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
54 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
62. Nanoparticle Simulations
• The simulation proceeds by the Metropolis algorithm:
– Each solvent cell is examined and an attempt is made to
convert from liquid to vapour (or vice-versa) with an
acceptance probability pacc = min[1, exp(-ΔH/kBT)]
– Similarly, the particles perform a random walk on wet areas
of the substrate, but cannot move into dry areas.
– The Hamiltonian from which ΔH is obtained is as follows:
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
55 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
63. Nanoparticle Simulations
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
56 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
64. Nanoparticle Simulations
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
56 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
65. Nanoparticle Simulations
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
57 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
66. Nanoparticle Simulations
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
57 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
67. Nanoparticle Simulations
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
58 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
68. Nanoparticle Simulations
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
58 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
69. A brief overview of Genetic Algorithms
Evolution
- Recombination (mating)
e.g. exchanging parameters
‘combine the best bits of each parent’
- Mutation
e.g. altering the value of a parameter at random with some small probability
GENERATION 0
TIME
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70. A brief overview of Genetic Algorithms
Evolution
- Recombination (mating)
e.g. exchanging parameters
‘combine the best bits of each parent’
- Mutation
e.g. altering the value of a parameter at random with some small probability
GENERATION 1
TIME
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71. A brief overview of Genetic Algorithms
Evolution
- Recombination (mating)
e.g. exchanging parameters
‘combine the best bits of each parent’
- Mutation
e.g. altering the value of a parameter at random with some small probability
GENERATION 1
TIME
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72. A brief overview of Genetic Algorithms
Evolution
- Recombination (mating)
e.g. exchanging parameters
‘combine the best bits of each parent’
- Mutation
e.g. altering the value of a parameter at random with some small probability
GENERATION 2
TIME
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73. A brief overview of Genetic Algorithms
Evolution
- Recombination (mating)
e.g. exchanging parameters
‘combine the best bits of each parent’
- Mutation
e.g. altering the value of a parameter at random with some small probability
GENERATION 2
TIME
Ben-Gurion University of the Negev
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74. A brief overview of Genetic Algorithms
Evolution
- Recombination (mating)
e.g. exchanging parameters
‘combine the best bits of each parent’
- Mutation
e.g. altering the value of a parameter at random with some small probability
GENERATION 3
TIME
Ben-Gurion University of the Negev
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75. A brief overview of Genetic Algorithms
Evolution
- Recombination (mating)
e.g. exchanging parameters
‘combine the best bits of each parent’
- Mutation
e.g. altering the value of a parameter at random with some small probability
GENERATION 3
TIME
Ben-Gurion University of the Negev
Distinguished Scientist Visitor Program
62 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
76. A brief overview of Genetic Algorithms
Evolution
- Recombination (mating)
e.g. exchanging parameters
‘combine the best bits of each parent’
- Mutation
e.g. altering the value of a parameter at random with some small probability
TIME
Ben-Gurion University of the Negev
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63 /81 Beer Sheva, Israel - 23/5 to 6/7 2009
Thursday, 25 June 2009
77. A brief overview of Genetic Algorithms
Evolution
- Recombination (mating)
e.g. exchanging parameters
‘combine the best bits of each parent’
- Mutation
e.g. altering the value of a parameter at random with some small probability
converges to
optimum solution
FITNESS
TIME
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78. Evolving towards a target pattern (simulated)
• Selected a target image from simulated data set
• Initialised GA
- Roulette Wheel selection
- Uniform crossover (probability 1)
- Random reset mutation (probability 0.3)
- Population size: 10
Target:
- Offspring: 5
- µ + λ replacement
• Ran the GA for 200 iterations
- on a single processor server, run time ≈ 5 days
- using Nottingham’s cluster (up to 10 nodes), run time ≈ 12 hours
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79. Evolving towards a target pattern (simulated)
Evolving to a simulated target
Target:
0.960
0.945
Fitness
0.930
Average
Best
0.915
0.900
0 2 4 6 8 11 15 19 23 27 31 35 39 43 47 51 55 59 63 67 71 75 79 83 87 91 95 99 104 110 116 122 128 134 140 146 152 158 164 170 176 182 188 194 200
Generations
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80. Evolving towards a target pattern (experimental)
Evolving to a experimental target Target:
1.000
0.975
Fitness
0.950
Average
Best
0.925
0.900
0 3 6 9 13 18 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 98 104 111 118 125 132 139 146 153 160 167 174 181 188 195
Generations
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81. Using only the same fitness function as for
the CAs was not sufficient for matching
simulation to experimental data
We extended the image analysis, i.e.
fitness function, to Minkowsky functionals,
namely, area, perimeter and euler
characteristic
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82. Self-organising nanostructures
Minkowski Functionals
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83. Self-organising nanostructures
Evolved design: Minkowski functionals
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84. Self-organising nanostructures
Evolved design: Minkowski functionals
Robustness checking
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85. Self-organising nanostructures
Evolved design: Minkowski functionals Robustness checking: i) Clustering
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86. Self-organising nanostructures
Evolved design: Minkowski functionals
Robustness checking: ii) Fitness Distance Correlation
1/Fitness
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87. Self-organising nanostructures
Evolved design: Minkowski functionals
Robustness checking: ii) Fitness Distance Correlation
1/Fitness
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88. Self-organising nanostructures
Evolved design: Minkowski functionals
Robustness checking: ii) Fitness Distance Correlation
1/Fitness
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89. Self-organising nanostructures
Experimental target set
Cell Island Labyrinth Worm
Evolved set
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90. Self-organising nanostructures
Experimental target set
Cell Island Labyrinth Worm
Evolved set
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91. Self-organising nanostructures
Experimental target set
Cell Island Labyrinth Worm
Evolved set
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92. Self-organising
nanostructures
Experimental target
set: Results
P.Siepmann, C.P. Martin,
I. Vancea, P.J. Moriarty, and
N. Krasnogor. A Genetic
Algorithm for Evolving Patterns in
Nanostructured systems.
Nano Letters (to appear)
The analysis of the
designability of specific
patterns is important as
some patterns are more
evolvable (multiple
solutions) than others and
Smart surface design
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93. Conclusions
• We can evolve target simulated behaviour using a GA with
the USM but the USM is not enough
•For evolving target experimental designs we used
Minkowsky functionals (e.g. Area, Perimeter, Euler
Characteristics)
• Using Fitness Distance Correlation and Clustering, we can
show whether a given fitness function is/isn’t an appropriate
objective function for a given domain.
• Can we generate a target spatio-temporal behaviour in a
CA/Real system?
YES
- GA generates very convincing designoid patterns
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94. Future Work (I)
use of more problem-specific fitness functions
open ended (multiobjective) evolution
e.g. “evolve a pattern with as many large spots as
possible in as ordered a fashion as possible”
parameter investigations
larger populations
full fitness landscape analysis
Noisy, expensive, multiobjective fitness functions
Datamining the results
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95. Future Work (II)
Collect Data Evolve models using
Evolutionary
“reality runs (RR)” results as targets
Expensive, noisy, Design for the models themselves
Stochastic, etc
Evolve parameters to
approximate target
behaviour of desired system
Physical, Chemical, Biological
Model
System Abstracted into
a model, e.g.,
ODE, NN, “cook book”,
etc Evolutionary
Design
Try best estimates from model parameters
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96. Applications (in design and manufacture) and further work
- Many, many systems can be modelled using CAs/Monte Carlos
-Many complex physical/chemical systems need to be programmed
- Research into chemical ‘design’
We are actively working towards these
practical goals in the context of the EPSRC
grant CHELLnet (EP/D023343/1), which
comprises
e.g. designoid patterns in the BZ reaction Evolvable CHELLware (EP/D021847/1),
vesiCHELL (EP/D022304/1),
brainCHELL (EP/D023645/1) and
wellCHELL (EP/D023807/1).
and self-organising nanostructured systems
CHELLNet
http://www.chellnet.org
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97. Acknowledgements
Prof. P. Moriarty (School of Physics and
Astronomy, UoN)
EPSRC, BBSRC for funding
BGU for funding the DSVP
Specially to Prof. Moshe Sipper for hosting
me at BGU!
Any questions?
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