Upload
natalio-krasnogor
View
1.272
Download
1
Embed Size (px)
Citation preview
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
An Evolutionary Algorithm Approach to Guiding the Evolution of Self-Organised Systems
1
Natalio KrasnogorInterdisciplinary Optimisation LaboratoryAutomated Scheduling, Optimisation & Planning Research GroupSchool of Computer Science
Centre for Integrative Systems BiologySchool of Biology
Centre for Healthcare Associated InfectionsInstitute of Infection, Immunity & Inflammation
University of Nottingham
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Previous Talk Slides At
http://www.slideshare.net/nxk
2Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Overview• Motivation
• Towards “Dial a Pattern” in Complex Systems
• Methodological Overview
• Virtual Complex Systems
• Physical Complex Systems
• Nanoparticle Simulation Details
• Results
• Conclusions & Further work
Au
3Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
4Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
- 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
Motivation
5Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
6Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
This has happened before in other research and industrial disciplines,e.g:
•VLSI design•Space antennae design•Transport Network design/optimisation•Personnel Rostering•Scheduling and timetabling
6Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
This has happened before in other research and industrial disciplines,e.g:
•VLSI design•Space antennae design•Transport Network design/optimisation•Personnel Rostering•Scheduling and timetabling
That is, complex systems are plagued with NP-Hardness, non-approximability, uncertainty, undecidability, etc results
6Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
This has happened before in other research and industrial disciplines,e.g:
•VLSI design•Space antennae design•Transport Network design/optimisation•Personnel Rostering•Scheduling and timetabling
That is, complex systems are plagued with NP-Hardness, non-approximability, uncertainty, undecidability, etc results
Yet, they are routinely solved by sophisticated optimisation and design techniques, like evolutionary algorithms, machine learning, etc
6Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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)
AnalyticalDesign
AutomatedDesign
(e.g. evolutionary)
Space of all possible designs/optimisations
A distinct view of the space of possible designs couldenhance the understanding of underlying system
7Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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.
8Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Towards “Dial a Pattern” in Complex Systems
9Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
D
iscr
ete
Lexi
cal S
truct
ures
Towards “Dial a Pattern” in Complex Systems
How do we program?
Distributed Disc
rete C.S
Continuous (simulated) CS
Discrete/Contin. (physical) CS
Discrete/Continuos (Biological)
9Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Dial a Pattern requires:
Parameter Learning/Evolution Technology
Structural Learning/Evolution Technology
Integrated Parameter/Structural Learning/Evolution Tech.
in silico or experimental implementation
Methodological Overview
10Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Initial Attempts at a “Dial a Pattern” Methodology
Evolutionaryalgorithms
behaviouremergent vs target
Parameters/Structure
CA-based / Real complex system
11Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Embodied Evolution Evolutionary Scheme Some parts of it are embedded into a
physical, chemical or biological substrate.
Genes Phenotypes Fitnesses
Variation & selection mechanisms(or other metaheuristic scheme)
Week embodiment
Strong embodiment
12Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
A Complex Mapping
13
Genotypes
Phenotypes
Fitness(es)
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
The CHELLnet: Unifying Investigation in Artificial Cellularity and Complexity
http://www.CHELLnet.org
14Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Life-like functionality through evolved complexity in 3 different platforms
15Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
- directing assembly of conducting networks so that there is function encoded in the structure of the product.
What is the CHELLnet?BrainCHELL
16Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
- complexity and pattern formation within lipid-bounded systems
What is the CHELLnet?VesiCHELL
17Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
- model miniature laboratory system with multiple chemical flow reactors where conditions of chemical processes computer controlled
What is the CHELLnet?WellCHELL
18Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
What is the CHELLnet?Evolvable CHELLware
Evolvable CHELLware
behaviouremergent vs target
Evolutionaryalgorithms parameters
CHELL platforms
brainCHELL
vesiCHELL
wellCHELL
19Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
The Evolutionary Engine
generic GA resultsspecialisedGA
XML
web-based configuration
module
Java servlet
web-based executionmodule
- no data types- no evaluation module- no parameters
- data types and bounds - evaluation module (‘plug in’) - EA or other ML parameters
Evaluationmodule
problem-specific
“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, brainCHELL, vesiCHELL) to specify the platform with which the EE will interact”
Evolvable CHELLware grant application
20Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
What is the CHELLnet?Evolvable CHELLware
21Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
What is the CHELLnet?Evolvable CHELLware
22Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
What is the CHELLnet?Evolvable CHELLware
Visual representation of best result
Visual representation
of target if applicable
Log details Results graph
23Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
What is the CHELLnet?Evolvable CHELLware
24Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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])
25Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Examples of Target Evolution in Complex systems
26Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
- 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.
?
- Self-organising processes
- Modelled using cellular automata, gass latice, ODEs, etc
Parameter Learning/Evolution Technology Example
27Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
CA continuous Turbulence
Gas Lattice
Gas Lattice
28Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
CA continuous Turbulence
Gas Lattice
Gas Lattice
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 ctend
;; ]end……..
Given
Evolve
d
Given
Evolve
d
28Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Wang Tiles Models
Glue Strength Matrix
Temperature T
29
Structural Learning/Evolution Technology Example
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Wang Tiles Models
Glue Strength Matrix
Temperature T Given
Evolve
d
29
Structural Learning/Evolution Technology Example
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/8130
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/8131
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Parameter Learning/Evolution Technology Example
mvaT-PAO1lecA-
Env.Params
32Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Parameter Learning/Evolution Technology Example
mvaT-PAO1lecA-
Env.Params
Evolve
d
Evolve
d32Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Evolutionaryalgorithms
behaviouremergent vs target
parameters
Complex System
How do we measure this?
How similar is to ?
33
How Do We Program These Complex Systems?
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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)
34Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
CA model USM
Fitness Distance Correlation
Clustering
GENOTYPE PHENOTYPE FITNESS
35Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
EXAMPLEturb_c4 refers to the spacio-temporal pattern produced by the fourth variation in parameter c of a Turbulence CA system
36Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Produced by MODEL(p1,p2,…,pn)
p1 p2 pn
37Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
• calculate a similarity matrix filled with the results of the application of the USM to a set of objects
• during the clustering process, similar objects should be grouped together
CA model USM
Fitness Distance Correlation
Clustering
GENOTYPE PHENOTYPE FITNESS
38Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/8139
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/8140
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
1 2 3
1 4 3
Fitness = USM (T,D)distance = 2
CA model USM
Fitness Distance Correlation
Clustering
GENOTYPE PHENOTYPE FITNESS
Target
Designoid
41Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/8142
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
generic GA resultsspecialisedGA
XML
web-based configuration
module
Java servlet
web-based executionmodule
- no data types- no evaluation module- no parameters
- data types and bounds - evaluation module (‘plug in’) - GA parameters
Evaluationmodule
problem-specific
43Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Motivation
- optimisation problems
- large search space
- inspired by Darwinian evolution
global optimum
A brief overview of Genetic Algorithms
22 0.25 1.0 4.5
phenotype
genotype
simulator fitness function1.05
fitness
- area covered?- degree of order?- similarity to target pattern?
44Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Results on CAs
.
e5
f3
Target Designoid
45Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Target usm(F,T) e(i) e(c) e(r) Ep 0.91980 0.26843 0.35314 0.05552 0.22569
.
Target Designoid
46Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
47Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
48Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Meta-Automaton (k=2, t=2)
Group 1 Group 2
k=2
Pha
se 1
Pha
se 2
t=2
49Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Target Designoid Target Designoid Target Designoid
Target Designoid Target Designoid
Evolving (k=1,2,t=1) Meta-Automaton
T D T D
50Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Evolving (k=4,t=1) Meta-Automaton
Target Designoid
Target Designoid
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
51Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Au
~3nm
Gold core
Thiol groups
Sulphur ‘head’
Alkane ‘tail’, e.g. octane
Dispersed in toluene, and spin castonto native-oxide-terminated silicon
Thiol-passivated Au nanoparticles
Self-Organised Nanostructured Systems
52Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
AFM images taken by Matthew O. Blunt, Nottingham
Au nanoparticles: Morphology
53Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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.
Nanoparticle Simulations
54
http://www.nottingham.ac.uk/physics/research/nano/
Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
• 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:
Nanoparticle Simulations
55Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Nanoparticle Simulations
56Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Nanoparticle Simulations
56Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Nanoparticle Simulations
57Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Nanoparticle Simulations
57Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Nanoparticle Simulations
58Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Nanoparticle Simulations
58Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
TIME
A brief overview of Genetic AlgorithmsEvolution- 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
59Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
TIME
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
60Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
TIME
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
60Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
TIME
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
61Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
TIME
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
61Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
TIME
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
62Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
TIME
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
62Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
TIME
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
63Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
TIME
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
FITN
ES
S
converges to optimum solution
63Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
• 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- 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
Target:
Evolving towards a target pattern (simulated)
64Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
0.900
0.915
0.930
0.945
0.960
02468 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
Evolving to a simulated target
Fitness
Generations
Average
Best
Target:
Evolving towards a target pattern (simulated)
65Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
0.900
0.925
0.950
0.975
1.000
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
Evolving to a experimental target
Fitness
Generations
AverageBest
Target:
Evolving towards a target pattern (experimental)
66Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
67Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresMinkowski Functionals
68Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresEvolved design: Minkowski functionals
69Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresEvolved design: Minkowski functionals Robustness checking
70Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresEvolved design: Minkowski functionals Robustness checking: i) Clustering
71Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresEvolved design: Minkowski functionals Robustness checking: ii) Fitness Distance Correlation
1/Fi
tnes
s
72Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresEvolved design: Minkowski functionals Robustness checking: ii) Fitness Distance Correlation
1/Fi
tnes
s
73Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresEvolved design: Minkowski functionals Robustness checking: ii) Fitness Distance Correlation
1/Fi
tnes
s
74Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresExperimental target set
Cell Island Labyrinth Worm
Evolved set
75Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresExperimental target set
Cell Island Labyrinth Worm
Evolved set
75Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresExperimental target set
Cell Island Labyrinth Worm
Evolved set
75Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Self-organising nanostructuresExperimental 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
76Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
• 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
Conclusions
77Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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
Future Work (I)
78Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
Future Work (II)
Physical, Chemical, BiologicalSystem
Expensive, noisy, Stochastic, etc
Model
EvolutionaryDesign
Evolve parameters toapproximate target behaviour of desired system
Try best estimates from model parameters
EvolutionaryDesign
Collect Data Evolve models using“reality runs (RR)” results as targetsfor the models themselves
Abstracted intoa model, e.g.,ODE, NN, “cook book”,etc
79Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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’
and self-organising nanostructured systems
e.g. designoid patterns in the BZ reaction
We are actively working towards these practical goals in the context of the EPSRC grant CHELLnet (EP/D023343/1), which comprises Evolvable CHELLware (EP/D021847/1), vesiCHELL (EP/D022304/1), brainCHELL (EP/D023645/1) and wellCHELL (EP/D023807/1).
CHELLNethttp://www.chellnet.org
80Thursday, 25 June 2009
Ben-Gurion University of the NegevDistinguished Scientist Visitor ProgramBeer Sheva, Israel - 23/5 to 6/7 2009/81
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?
81Thursday, 25 June 2009