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Transcript of Artificial Immune Systems Razieh Khamseh-Ashari Department of Electrical and Computer Eng Isfahan...
![Page 1: Artificial Immune Systems Razieh Khamseh-Ashari Department of Electrical and Computer Eng Isfahan University of Technology Supervisor: Dr. Abdolreza Mirzaei.](https://reader038.fdocuments.in/reader038/viewer/2022103122/56649ccc5503460f94995b5a/html5/thumbnails/1.jpg)
Artificial Immune Systems
Razieh Khamseh-Ashari
Department of Electrical and Computer Eng
Isfahan University of Technology
Supervisor: Dr. Abdolreza Mirzaei
![Page 2: Artificial Immune Systems Razieh Khamseh-Ashari Department of Electrical and Computer Eng Isfahan University of Technology Supervisor: Dr. Abdolreza Mirzaei.](https://reader038.fdocuments.in/reader038/viewer/2022103122/56649ccc5503460f94995b5a/html5/thumbnails/2.jpg)
Introduction to the Immune System
Artificial Immune Systems
A Framework to Design Artificial
Immune Systems (AIS)
Representation Schemes
Affinity Measures
Immune Algorithms
Outline
2Artificial Immune Systems
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Introduction to the Immune System
Artificial Immune Systems
A Framework to Design Artificial
Immune Systems (AIS)
Representation Schemes
Affinity Measures
Immune Algorithms
Outline
3Artificial Immune Systems
![Page 4: Artificial Immune Systems Razieh Khamseh-Ashari Department of Electrical and Computer Eng Isfahan University of Technology Supervisor: Dr. Abdolreza Mirzaei.](https://reader038.fdocuments.in/reader038/viewer/2022103122/56649ccc5503460f94995b5a/html5/thumbnails/4.jpg)
What is the Immune System ?
a complex system of cellular and molecular components having the primary function of distinguishing self from not self and defense against foreign organisms or substances (Dorland's Illustrated Medical Dictionary)
The immune system is a cognitive system whose primary role is to provide body maintenance (Cohen)
Immune system was evolutionary selected as a consequence of its first and primordial function to provide an ideal inter-cellular communication pathway (Stewart)
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1. Immune System 2. AIS Concepts 3. Designing a Framework
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Artificial Immune Systems 5
Lymphatic vessels
Lymph nodes
Thymus
Spleen
Tonsils andadenoids
Bone marrow
Appendix
Peyer’s patches
Primary lymphoidorgans
Secondary lymphoidorgans
1. Immune System 2. AIS Concepts 3. Designing a Framework
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Classical Immunity
The purpose of the immune system is
defence
Innate and acquired immunity
Innate is the first line of defense. Germ line
encoded (passed from parents) and is quite
‘static’ (but not totally static)
Adaptive (acquired). Somatic (cellular) and is
acquired by the host over the life time. Very
dynamic.
These two interact and affect each other
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Multiple layers of the immune system
Phagocyte
Adaptive immune
response
Lymphocytes
Innate immune
response
Biochemical barriers
Skin
Pathogens
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Innate Immunity
May take days to remove an infection, if
it fails, then the adaptive response may
take over
Macrophages and neurophils are actors
Other actors such as TLR’s and
dendritic cells (next lecture) are
essential for recognition8Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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Adaptive Immune System
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Lymphocytes
Carry antigen receptors that are specific
They are produced in the bone marrow
through
B and T Cells are the main actors of the
adaptive immune system
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B Cell Pattern Recognition
B cells have receptors called antibodies The immune recognition is based on the
complementarity between the binding region of the receptor and a portion of the antigen calledthe epitope.
Recognition is not just by a single antibody, but a collection of them Learn not through a single agent, but
multiple ones
Epitopes
B-cell Receptors (Ab)
Antigen
B-cell
BCR or Antibody
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Processes within the Immune System (very basically)
Negative Selection
Censoring of T-cells in the thymus gland of
T-cells that recognise self• Defining normal system behavior
Clonal Selection
Proliferation and differentiation of cells
when they have recognised something• Generalise and learn
Self vs Non-Self
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Clonal Selection
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Clonal Selection
Foreign antigens
Proliferation(Cloning)
Differentiation
Plasma cells
Memory cellsSelection
M
M
Antibody
Self-antigen
Self-antigen
Clonal deletion(negative selection)
Clonal deletion(negative selection)
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Affinity Maturation
Responses mediated by T cells improve
with experience
Mutation on receptors (hypermutation and
receptor editing)
During the clonal expansion, mutation can
lead to increased affinity, these new ones are
selected to enter a ‘pool’ of memory cells• Can also lead to bad ones and these are deleted
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Immune Responses
Antigen Ag1 Antigens Ag1, Ag2
Primary Response Secondary Response
Lag
Response to Ag1
Ant
ibod
y C
once
ntra
tion
Time
Lag
Response to Ag2
Response to Ag1
...
...
Cross-Reactive Response
...
...
Antigen Ag1 + Ag3
Response to Ag1 + Ag3
Lag
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Summary
Innate and adaptive immunity Focused on adaptive here
Lymphocytes Negative selection Clonal selection Immune memory and learning
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Further Immunology and Modelling
1. Immune System 2. AIS Concepts 3. Designing a Framework
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What is the Immune System ?
• The are many different viewpoints
• These views are not mutually exclusive
• Lots of common ingredients
classical
networkdanger
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Problems with the classical view
What happens if self changes?
What about things that are “not harmful”
The Danger model was proposed
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Danger theory (Matzinger 1994)
it is not “non-self”, but “danger” that the IS recognises dangerous invaders cause cell death or
stress these cells generate “danger signal”
molecules• unlike natural cell death
these stimulate an immune response local to the danger
• to identify the “culprit”
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Immune Network Theory
Idiotypic network (Jerne, 1974)
B cells co-stimulate each other
Treat each other a bit like antigens
Creates an immunological memory
1
2
3
Ag
A ctiva tionP ositive response
S uppress ionN ega tive response
A ntibody
P ara tope
Id io tope
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Introduction to the Immune System
Artificial Immune Systems
Remarkable Immune Properties
Concepts, Scope and Applications
Brief History of AIS
A Framework to Design Artificial
Immune Systems (AIS)
Representation Schemes
Affinity Measures
Immune Algorithms
Outline
23Artificial Immune Systems
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Artificial Immune Systems: A Definition
AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models, which are applied to complex problem domains
[De Castro and Timmis,2002]
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Concepts
Specificity Diversity Clonal selection Affinity maturation Immunity memory Positive and negative selection Distributing ability Multi-layering Self-organization Anomaly detection
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Scope of AIS
Pattern recognition
Fault and anomaly detection
Data analysis (classification, clustering, etc.)
Agent-based systems
Search and optimization
Machine-learning
Autonomous navigation and control
Artificial life
Security of information systems26Artificial Immune Systems
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Scope of AIS
Clu
ster
ing/
clas
sifi
cat i
on
Ano
mal
y de
tect
ion
Com
p ute
r se
curi
ty
Opt
imis
atio
n
Lea
rnin
g
Bi o
i nfo
rmat
ics
I mag
e pr
oc.
Ro b
oti c
s
Co n
trol
Web
mi n
i ng
0
10
20
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The Early Days:
Developed from the field of theoretical immunology in the mid
1980’s.
1990 – Bersini first use of immune algorithms to solve problems
Forrest et al – Computer Security mid 1990’s
Work by IBM on virus detection
Hunt et al, mid 1990’s – Machine learning
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Introduction to the Immune System
Artificial Immune Systems
Remarkable Immune Properties
Concepts, Scope and Applications
Brief History of AIS
A Framework to Design Artificial
Immune Systems (AIS)
Representation Schemes
Affinity Measures
Immune Algorithms
Outline
29Artificial Immune Systems
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A Framework for AIS
Algorithms
Affinity
Representation
Application
Solution
AIS
Shape-Space
Binary
Integer
Real-valued
Symbolic
[De Castro and Timmis, 2002]
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Shape-Space
An antibody can recognise any antigen whose complement lies within a small surrounding region of width 𝞮(the cross-reactivity threshold)
This results in a volume v𝞮 known as the recognition region of the antibody
𝞮
v𝞮
V
S
v𝞮
v𝞮
𝞮
𝞮
[Perelson,1989]
31
Shape space(or solution space)
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Choice of Representation
Assume the general case: Ab = Ab1, Ab2, ..., AbLAg = Ag1, Ag2, ..., AgL
Binary representation Matching by bits
Continuous (numeric) Real or Integer, typically Euclidian
Categorical (nominal) E.g female or male of the attribute Gender.
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Representation – Shape Space
Used for modeling antibody and antigen Determine a measure to calculate affinity
Hamming shape space(binary)
if Abi != Agi: 0 otherwise (XOR operator)
Antigen
Antibody
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A Framework for AIS
Algorithms
Affinity
Representation
Application
Solution
AIS Euclidean
Manhattan
Hamming
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Affinities: related to distance/similarity Examples of affinity measures
Euclidean
Manhattan
Hamming
L
iii AgAbD
1
2)(
L
iii AgAbD
1
L
i
ii AgAbD
1 otherwise0
if1δwhereδ,
Affinity
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Affinities in Hamming Shape-Space
(a) Hamming distance
(b) r-contiguous bits rule
XOR : Affinity: 6
0 0 1 1 0 0 1 1
1 1 1 0 1 1 0 1
1 1 0 1 1 1 1 0
XOR :
0 0 1 1 0 0 1 1
1 1 1 0 1 1 0 1
1 1 0 1 1 1 1 0
Affinity: 4
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Mutation - Binary
1 0 0 0 1 1 1 0 Original string
Mutated string
Bit to be mutated
1 0 0 0 0 1 1 0
Single-point mutation
1 0 0 0 1 1 1 0
0 0 0 0 0 1 1 0
Multi-point mutation
Original string
Mutated string
Bits to be mutated
Single point mutation
Multi-point mutation37Artificial Immune Systems
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Affinity Proportional Mutation
Affinity maturation is controlled Proportional to
antigenic affinity (D*) = exp(-D*) =mutation rate D*= affinity =control parameter
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
D*
5ت=ت
10ت=ت
20ت=ت
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A Framework for AIS
Algorithms
Affinity
Representation
Application
Solution
AIS
Bone Marrow Models
Clonal Selection
Negative Selection
Positive Selection
Immune Network Models
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The Algorithms Layer
Bone Marrow Models
Clonal Selection CLONALG
AIRS
Negative Selection
Positive Selection
Network Models AINE
aiNET
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The Algorithms Layer
Bone Marrow Models
Clonal Selection CLONALG
AIRS
Negative Selection
Positive Selection
Network Models AINE
aiNET
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Bone Marrow Models
Gene libraries are used to create antibodies from the bone marrow
Use this idea to generate attribute strings that represent receptors
Antibody production through a random concatenation from gene libraries
An individual genome corresponds to four libraries: Library 1 Library 2 Library 3 Library 4
A1 A2 A3 A4 A5 A6 A7 A8
A3 D5 C8 B2
A3 D5 C8 B2
A3 B2 C8 D5 Expressed Ab molecule
B1 B2 B3 B4 B5 B6 B7 B8 C1 C2 C3 C4 C5 C6 C7 C8 D1 D2 D3 D4 D5 D6 D7 D8
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Clonal Selection –CLONALG
1. Initialisation
2. Antigenic presentation
a. Affinity evaluation
b. Clonal selection and expansion
c. Affinity maturation
d. Metadynamics
3. Cycle 43Artificial Immune Systems
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1. Initialisation
2. Antigenic presentation
a. Affinity evaluation
b. Clonal selection and expansion
c. Affinity maturation
d. Metadynamics
3. Cycle
CLONALG
Create a random population of individuals (P)
44Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation
2. Antigenic presentation
a. Affinity evaluation
b. Clonal selection and expansion
c. Affinity maturation
d. Metadynamics
3. Cycle
CLONALG
For each antigenic pattern in the data-set S do:
45Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation2. Antigenic
presentation
a. Affinity evaluation
b. Clonal selection and expansion
c. Affinity maturation
d. Metadynamics
3. Cycle
CLONALG
Present it to the population P and determine its affinity with each element of the population
46Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation
2. Antigenic presentation
a. Affinity evaluation
b. Clonal selection and expansion
c. Affinity maturation
d. Metadynamics
3. Cycle
CLONALG
Select n highest affinity elements of P
Generate clones proportional to their affinity with the antigen
(higher affinity=more clones)
47Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation
2. Antigenic presentationa. Affinity evaluation
b. Clonal selection and expansion
c. Affinity maturation
d. Metadynamics
3. Cycle
CLONALG
Mutate each clone High affinity=low mutation rate
and vice-versa Add mutated individuals to
population P Reselect best individual to be
kept as memory m of the antigen presented
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1. Immune System 2. AIS Concepts 3. Designing a Framework
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CLONALG
1. Initialisation
2. Antigenic presentation
a. Affinity evaluation
b. Clonal selection and expansion
c. Affinity maturation
d. Metadynamics
3. Cycle
Replace a number r of individuals with low affinity with randomly generated new ones
49Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation
2. Antigenic presentation
a. Affinity evaluation
b. Clonal selection and expansion
c. Affinity maturation
d. Metadynamics
3. Cycle
CLONALG
Repeat step 2 until a certain stopping criteria is met
50Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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CLONALG
nci =round((β*nh )/i)
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1. Immune System 2. AIS Concepts 3. Designing a Framework
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Negative Selection Algorithms
Forrest 1994: Idea taken from the negative selection of T-cells in the thymus
Applied initially to computer security
Artificial Immune Systems 52
Selfstrings (S)
Generaterandom strings
(R0)Match Detector
Set (R)
Reject
No
Yes
No
Yes
Detector Set ( R )
Strings (e.g. credit card use patterns)
Match
Non-self Detected
Developing the detector set
Using the detector set
1. Immune System 2. AIS Concepts 3. Designing a Framework
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Training ALCs with negative selection
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1. Immune System 2. AIS Concepts 3. Designing a Framework
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Artificial Immune Network
Timmis and Neal,2000
The ALCs interact with each other to learn the structure of a non-
self pattern
The ALCs in a network co-stimulates and/or co-suppress each
other to adapt to the non-self pattern
The stimulation level
antigen stimulation
network stimulation
network suppression
Artificial Immune Systems 54
1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation2. Antigenic presentation
a. Affinity evaluationb. Clonal selection and
expansionc. Affinity maturationd. Metadynamicse. Clonal suppression
3. Network interactions4. Network suppression5. Diversity6. Cycle
aiNET
For each antigenic pattern in the data-set S do:
55Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation2. Antigenic presentation
a. Affinity evaluationb. Clonal selection and
expansionc. Affinity maturationd. Metadynamicse. Clonal suppression
3. Network interactions4. Network suppression5. Diversity6. Cycle
aiNET
Present it to the population P and determine its affinity with each element of the population
56Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation2. Antigenic presentation
a. Affinity evaluationb. Clonal selection and
expansionc. Affinity maturationd. Metadynamicse. Clonal suppression
3. Network interactions4. Network suppression5. Diversity6. Cycle
aiNET
Select n highest affinity elements of P
Generate clones proportional to their affinity with the antigen
(higher affinity=more clones)
57Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation
2. Antigenic presentationa. Affinity evaluation
b. Clonal selection and expansion
c. Affinity maturation
d. Metadynamics
e. Clonal suppression
3. Network interactions
4. Network suppression
5. Diversity
6. Cycle
aiNET
Mutate each clone High affinity=low mutation rate
and vice-versa Select h highest affinity cells
and place into memory set
58Artificial Immune Systems
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aiNET
1. Initialisation2. Antigenic presentation
a. Affinity evaluationb. Clonal selection and
expansionc. Affinity maturationd. Metadynamicse. Clonal suppression
3. Network interactions4. Network suppression5. Diversity6. Cycle
Eliminate all memory clones whose affinity with the antigen is less than a predefined threshold
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1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation2. Antigenic presentation
a. Affinity evaluationb. Clonal selection and
expansionc. Affinity maturationd. Metadynamicse. Clonal suppression
3. Network interactions4. Network suppression5. Diversity6. Cycle
aiNET
Determine similarity between each pair of network antibodies
60Artificial Immune Systems
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1. Initialisation2. Antigenic presentation
a. Affinity evaluationb. Clonal selection and
expansionc. Affinity maturationd. Metadynamicse. Clonal suppression
3. Network interactions4. Network suppression5. Diversity6. Cycle
aiNET
Eliminate all network antibodies whose affinity is less than a pre-defined threshold
61Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation2. Antigenic presentation
a. Affinity evaluationb. Clonal selection and
expansionc. Affinity maturationd. Metadynamicse. Clonal suppression
3. Network interactions4. Network suppression5. Diversity6. Cycle
aiNET
Introduce a random number of new antibodies into P
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1. Immune System 2. AIS Concepts 3. Designing a Framework
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1. Initialisation2. Antigenic presentation
a. Affinity evaluationb. Clonal selection and
expansionc. Affinity maturationd. Metadynamicse. Clonal suppression
3. Network interactions4. Network suppression5. Diversity6. Cycle
aiNET
Repeat 2 - 5 for a pre-defined number of iterations
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aiNET
64Artificial Immune Systems
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aiNET
66Artificial Immune Systems
1. Immune System 2. AIS Concepts 3. Designing a Framework
The aiNet model uses the minimal spanning tree of the formed weighted-edge graph or hierarchical agglomerative clustering to determine the structure of the network clusters in the graph.
The stopping condition of the while-loop can be one of the following
Setting an iteration counter
Setting the maximum size of the network
Testing for convergence
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aiNET on Data Mining
Limited visualisation
Interpret via MST or dendrogram
Compression rate of 81%
Successfully identifies the clusters
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Training Patterns
Training Pattern
Result immune network
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aiNET on multimodal optimisation
Initial population
Final population
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1. Immune System 2. AIS Concepts 3. Designing a Framework
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Results – Multi Function
aiNET CLONALG
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Reference
L. N. de Castro, J. I. Timmis, Artificial immune systems as a novel
soft computing paradigm, Soft Computing 7 (2003) 526–544
Adndries P.Engelbrecht ,Computational Intelligence An introduction
D. Dasguptaa, S.Yua, et al, Recent Advances in Artificial Immune
Systems: Models and Applications, Applied Soft Computing 11
(2011) 1574–1587.
J. Zheng ,Y. Chen, A Survey of artificial immune applications, Artif
Intell Rev (2010) 34:19–34
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Thank You
71Artificial Immune Systems
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Questions?
72Artificial Immune Systems