Download Collaborative Bioinspired Algorithms

Artificial Immune Systems
Razieh Khamseh-Ashari
Department of Electrical and Computer Eng
Isfahan University of Technology
Supervisor: Dr. Abdolreza Mirzaei
Outline

Introduction to the Immune System

Artificial Immune Systems

A Framework to Design Artificial Immune Systems (AIS)

Representation Schemes

Affinity Measures

Immune Algorithms
Artificial Immune Systems
2
Outline

Introduction to the Immune System

Artificial Immune Systems

A Framework to Design Artificial Immune Systems (AIS)

Representation Schemes

Affinity Measures

Immune Algorithms
Artificial Immune Systems
3
1. Immune System
2. AIS Concepts
3. Designing a Framework
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)
Artificial Immune Systems
4
1. Immune System
Primary lymphoid
organs
2. AIS Concepts
3. Designing a Framework
Secondary lymphoid
organs
Tonsils and
adenoids
Thymus
Spleen
Peyer’s patches
Appendix
Bone marrow
Lymph nodes
Lymphatic vessels
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
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
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Multiple layers of the immune system
Pathogens
Skin
Biochemical
barriers
Phagocyte
Innate
immune
response
Lymphocytes
Adaptive
immune
response
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
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 recognition
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Adaptive Immune System
Artificial Immune Systems
9
1. Immune System
2. AIS Concepts
3. Designing a Framework
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
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
B Cell Pattern Recognition
BCR or Antibody


B-cell
B-cell Receptors (Ab)
Epitopes

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 called
the epitope.
Recognition is not just by a single antibody,
but a collection of them

Antigen
Learn not through a single agent, but
multiple ones
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
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
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Clonal Selection
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Clonal Selection
Clonal deletion
(negative selection)
Self-antigen
Proliferation
(Cloning)
M
M
Antibody
Memory cells
Selection
Differentiation
Plasma cells
Foreign antigens
Self-antigen
Clonal deletion
(negative selection)
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
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
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Immune Responses
Antibody Concentration
Cross-Reactive
Response
Secondary Response
Primary Response
Lag
Lag
Response
to Ag1
Lag
Response
to Ag1
Response
to Ag2
...
...
Antigen Ag1
...
Response to
Ag1 + Ag3
...
Antigens
Ag1, Ag2
Artificial Immune Systems
Antigen
Ag1 + Ag3
Time
16
1. Immune System
2. AIS Concepts
3. Designing a Framework
Summary


Innate and adaptive immunity
Focused on adaptive here




Lymphocytes
Negative selection
Clonal selection
Immune memory and learning
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Further Immunology and Modelling
1. Immune System
2. AIS Concepts
3. Designing a Framework
What is the Immune System ?
• The are many different
viewpoints
• These views are not mutually
exclusive
classical
• Lots of common ingredients
danger
network
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Problems with the classical view

What happens if self changes?

What about things that are “not harmful”

The Danger model was proposed
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
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”
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Immune Network Theory

Idiotypic network (Jerne, 1974)

B cells co-stimulate each other


Treat each other a bit like antigens
Creates an immunological memory
Suppression
Negative response
Paratope
Ag
1
2
Idiotope
3
Antibody
Activation
Positive response
Artificial Immune Systems
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Outline

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
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
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]
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Concepts










Specificity
Diversity
Clonal selection
Affinity maturation
Immunity memory
Positive and negative selection
Distributing ability
Multi-layering
Self-organization
Anomaly detection
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
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 Artificial
systems
Immune Systems
26
1. Immune System
2. AIS Concepts
3. Designing a Framework
Scope of AIS
20
10
0
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
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
Artificial Immune Systems
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Outline

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
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
A Framework for AIS
Solution
Shape-Space
Algorithms
AIS
Binary
Integer
Affinity
Real-valued
Symbolic
Representation
Application
[De Castro and Timmis, 2002]
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Shape-Space


V
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
v𝞮
𝞮
𝞮
v𝞮
𝞮
v𝞮
S
Shape space
(or solution space)
Artificial Immune Systems
[Perelson,1989]
31
1. Immune System
2. AIS Concepts
3. Designing a Framework
Choice of Representation

Assume the general case:
Ab = Ab1, Ab2, ..., AbL
Ag = Ag1, Ag2, ..., AgL

Binary representation


Continuous (numeric)


Matching by bits
Real or Integer, typically Euclidian
Categorical (nominal)

E.g female or male of the attribute Gender.
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Representation – Shape Space


Used for modeling antibody and antigen
Determine a measure to calculate affinity
Antigen
Antibody

Hamming shape space(binary)
if Abi != Agi: 0 otherwise (XOR operator)
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
A Framework for AIS
Solution
Algorithms
AIS
Euclidean
Affinity
Manhattan
Hamming
Representation
Application
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Affinity


Affinities: related to distance/similarity
Examples of affinity measures

Euclidean
L
 ( Ab  Ag )
D
i 1
i
2
i
L

Manhattan
D   Abi  Agi
i 1
L

Hamming
D   δ, where
i 1
1 if Abi  Agi
δ
0 otherwise
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Affinities in Hamming Shape-Space

(a) Hamming distance
0 0
1
1 0
0
1
1
1 1
1
0 1
1
0
1
XOR 1 1 0 1 1 1 1 0
:
Affinity: 6

(b) r-contiguous bits rule
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
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Mutation - Binary

Single point mutation

Multi-point mutation
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
Affinity Proportional Mutation

1
0.9
0.8
0.7
‫ت=ت‬5
0.6

0.5
‫ت=ت‬10
0.4
Affinity maturation is
controlled
 Proportional to
antigenic affinity
 (D*) = exp(-D*)
  =mutation rate
 D*= affinity
  =control parameter
‫ت=ت‬20
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
D*
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
A Framework for AIS
Solution
Bone Marrow Models
Algorithms
Clonal Selection
Negative Selection
AIS
Affinity
Representation
Positive Selection
Immune Network
Models
Application
Artificial Immune Systems
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1. Immune System
2. AIS Concepts
3. Designing a Framework
The Algorithms Layer

Bone Marrow Models

Clonal Selection

CLONALG

AIRS

Negative Selection

Positive Selection

Network Models

AINE

aiNET
Artificial Immune Systems
40
1. Immune System
2. AIS Concepts
3. Designing a Framework
The Algorithms Layer

Bone Marrow Models

Clonal Selection

CLONALG

AIRS

Negative Selection

Positive Selection

Network Models

AINE

aiNET
Artificial Immune Systems
41
1. Immune System
2. AIS Concepts
3. Designing a Framework
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
A1 A2 A3 A4 A5 A6 A7 A8
A3
Library 2
Library 3
B1 B2 B3 B4 B5 B6 B7 B8
Library 4
C1 C2 C3 C4 C5 C6 C7 C8
B2
C8
A3
B2
C8
D1 D2 D3 D4 D5 D6 D7 D8
D5
D5
A3 B2 C8 D5
Expressed Ab molecule
Artificial Immune Systems
42
1. Immune System
2. AIS Concepts
3. Designing a Framework
Clonal Selection –CLONALG
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
3.
Affinity evaluation
Clonal selection and expansion
Affinity maturation
Metadynamics
Cycle
Artificial Immune Systems
43
1. Immune System
2. AIS Concepts
3. Designing a Framework
CLONALG
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
3.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics

Create a random
population of
individuals (P)
Cycle
Artificial Immune Systems
44
1. Immune System
2. AIS Concepts
3. Designing a Framework
CLONALG
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
3.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics

For each antigenic
pattern in the data-set S
do:
Cycle
Artificial Immune Systems
45
1. Immune System
2. AIS Concepts
3. Designing a Framework
CLONALG
Initialisation
Antigenic presentation
1.
2.
a.
Affinity evaluation
b.
Clonal selection and
expansion
Affinity maturation
Metadynamics
c.
d.
3.

Present it to the population P
and determine its affinity with
each element of the population
Cycle
Artificial Immune Systems
46
1. Immune System
2. AIS Concepts
3. Designing a Framework
CLONALG
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
3.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics
Select n highest affinity
elements of P
 Generate clones proportional
to their affinity with the
antigen
(higher affinity=more clones)

Cycle
Artificial Immune Systems
47
1. Immune System
2. AIS Concepts
3. Designing a Framework
CLONALG
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
3.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics
Cycle




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
Artificial Immune Systems
48
1. Immune System
2. AIS Concepts
3. Designing a Framework
CLONALG
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
3.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics

Replace a number r of
individuals with low affinity with
randomly generated new ones
Cycle
Artificial Immune Systems
49
1. Immune System
2. AIS Concepts
3. Designing a Framework
CLONALG
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
3.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics
Cycle

Repeat step 2 until a certain
stopping criteria is met
Artificial Immune Systems
50
1. Immune System
2. AIS Concepts
3. Designing a Framework
CLONALG
nci =round((β*nh )/i)
Artificial Immune Systems
51
1. Immune System
2. AIS Concepts
3. Designing a Framework
Negative Selection Algorithms


Forrest 1994: Idea taken from the negative selection of T-cells in
the thymus
Applied initially to computer security
Developing the
detector set
Generate
random strings
(R0)
Using the
detector set
Self
strings (S)
Match
No
Detector
Set (R)
Strings (e.g.
credit card use
patterns)
Yes
Detector Set
(R)
Match
No
Yes
Reject
Non-self
Detected
Artificial Immune Systems
52
1. Immune System
2. AIS Concepts
3. Designing a Framework
Training ALCs with negative selection
Artificial Immune Systems
53
1. Immune System
2. AIS Concepts
3. Designing a Framework
Artificial Immune Network

Timmis and Neal,2000

The ALCs interact with each other to learn the structure of a nonself 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
aiNET
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
e.
3.
4.
5.
6.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics
Clonal suppression

For each antigenic pattern in
the data-set S do:
Network interactions
Network suppression
Diversity
Cycle
Artificial Immune Systems
55
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
e.
3.
4.
5.
6.

Affinity evaluation
Clonal selection and expansion
Affinity maturation
Metadynamics
Clonal suppression
Present it to the population P
and determine its affinity with
each element of the population
Network interactions
Network suppression
Diversity
Cycle
Artificial Immune Systems
56
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
e.
3.
4.
5.
6.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics
Clonal suppression
Select n highest affinity
elements of P
 Generate clones proportional
to their affinity with the
antigen
(higher affinity=more clones)

Network interactions
Network suppression
Diversity
Cycle
Artificial Immune Systems
57
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
e.
3.
4.
5.
6.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics
Clonal suppression



Mutate each clone
High affinity=low mutation rate
and vice-versa
Select h highest affinity cells
and place into memory set
Network interactions
Network suppression
Diversity
Cycle
Artificial Immune Systems
58
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
e.
3.
4.
5.
6.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics
Clonal suppression
Network interactions
Network suppression
Diversity
Cycle

Eliminate all memory clones whose affinity
with the antigen is less than a predefined
threshold
Artificial Immune Systems
59
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
e.
3.
4.
5.
6.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics
Clonal suppression
Network interactions
Network suppression
Diversity
Cycle

Determine similarity between each
pair of network antibodies
Artificial Immune Systems
60
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
e.
3.
4.
5.
6.
Affinity evaluation
Clonal selection and expansion
Affinity maturation
Metadynamics
Clonal suppression
Network interactions
Network suppression
Diversity
Cycle

Eliminate all network
antibodies whose affinity is
less than a pre-defined
threshold
Artificial Immune Systems
61
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
e.
3.
4.
5.
6.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics
Clonal suppression
Network interactions
Network suppression
Diversity
Cycle

Introduce a random number
of new antibodies into P
Artificial Immune Systems
62
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET
Initialisation
Antigenic presentation
1.
2.
a.
b.
c.
d.
e.
3.
4.
5.
6.
Affinity evaluation
Clonal selection and
expansion
Affinity maturation
Metadynamics
Clonal suppression
Network interactions
Network suppression
Diversity
Cycle

Repeat 2 - 5 for a pre-defined
number of iterations
Artificial Immune Systems
63
aiNET
Artificial Immune Systems
64
1. Immune System
2. AIS Concepts
3. Designing a Framework
Artificial Immune Systems
65
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET

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
Artificial Immune Systems
66
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET on Data Mining




Limited visualisation
Interpret via MST or
dendrogram
Compression rate of 81%
Successfully identifies the
clusters
Training Pattern
Training Patt erns
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
11
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
5 55
1
1
1
1
1
2
1
2
1
1
1
2
1
1
1
1
2
1
2
2
2
2
5
1
1
5
2
2
1
1
5
5
2
2
5
5
1
1
1
2
2
2
5
5
2
5
5
5
5
5
2
2
1
1
5
5
5
5
2
2
2
2
2
2
5
5
5
2
2
1
5
5
5
5
5
1
2
2
1
1
1
1
5
5
5
5
1
1
1
2
2
66
5
5
1
2
2
2
2
6
6
5
5
5
5
2
2
2
66
6
6 55
5
5
5
2
2
1
1
1
2
2
2
22
2
5
1
1
1
2
5
2
2
5
5
5
2
2
2
2
1
5
5
1
1
1
2
2
5
5
5
5
5
5
1
2
2
2
5
5
5
1
1
2
2
2
1
1
5
2
2
1
5
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2
2
2
2
5
5
2
1
1
1
5
5
1
5
5
1
5
5
1
5
5
1
2 2
1
1
1
1
1
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.8
0.6
0.4
0.2
0
4 4
4444 44
444
4
4
4
4
4
4
4
4
4
4
4
44
4
4
4
4
4
4
4
4
4
4
4
4
44
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
44
4
4
4
4
4
4
4
4
444
4
443
3
3
3 3
33
3
3
3 3
3
3
33
33
3
3
3
3
33
3
3
3
3
33
3
3
3
3
3
3
3
3
3
33
3
3
3
3
3
3
3
3
3
3
33
3
3
3
33
3
3
3
3
3
3
3
3
3
3
3
3
3
3
33
3
33
3
3 3
3
3
3
3
33
0
0.2
8
8
8
8
88
8
8
7
8
8
7
7
8
8
7
7
8
7
8
8
7
7
88
8
7
8
8
7
8
777
8
7
7
88
8
77
8
7
8
8
7
7
7
88
8
7
8
7
7
7
8
8
8
7
7
7
8
7
7
8
8
7
8
88
7
8
777
8
8
7
8
8
8
77
8
8
8
8
8
77
8
7
8
77
7
7
7
8
7
7
0.4
0.6
0.8
1
Result immune network
Artificial Immune Systems
67
1. Immune System
2. AIS Concepts
3. Designing a Framework
aiNET on multimodal optimisation
Initial population
Final population
Artificial Immune Systems
68
1. Immune System
2. AIS Concepts
3. Designing a Framework
Results – Multi Function
aiNET
CLONALG
Artificial Immune Systems
69
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
Artificial Immune Systems
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Thank You
Artificial Immune Systems
71
Questions?
Artificial Immune Systems
72