artificial immune systems
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BY: Nazanin Asadi Zohre Molaei. Artificial Immune Systems. Isfahan University of Technology. Outline. History Natural Immune System Artificial Immune System Application Experiment Result Reference. History. Developed from the field of theoretical immunology in the mid 1980’s. - PowerPoint PPT PresentationTRANSCRIPT
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ARTIFICIAL IMMUNE SYSTEMS
BY:Nazanin AsadiZohre Molaei
Isfahan University of Technology
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Outline
History Natural Immune System Artificial Immune System Application Experiment Result Reference
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History
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
Hunt et al, mid 1990’s – Machine learning
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Basic Immunology
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Role of the Immune System
Protect our bodies from infection Primary immune response
Launch a response to invading pathogens
Secondary immune responseRemember past encountersFaster response the second time around
The IS is adaptable (presents learning and memory)
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Where is it ?
Lymphatic vessels
Lymph nodes
Thymus
Spleen
Tonsils andadenoids
Bone marrow
Appendix
Peyer’s patches
Primary lymphoidorgans
Secondary lymphoidorgans
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Lymphocytes
Carry antigen receptors that are specificThey are produced in the bone marrow through random re-arrangement
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 called the epitope.
Recognition is not just by a single
antibody, but a collection of them
Learn not through a single agent,
but multiple ones
B-cell
BCR or Antibody
Epitopes
B-cell Receptors (Ab)
Antigen
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T-cells
Regulation of other cells
Active in the immune response
Helper T-cells
Killer T-cells
T-cell
TCR
APC
MHC-II Protein Antigen
Peptide
TH cell
MHC/peptide complex
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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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The Immune System models•The are many different viewpoints•These views are not mutually exclusive
classical
networkdanger
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Artificial Immune Systems
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Basic concepts
trained detectors(artificial lymphocytes) that detect nonself
patterns
need a good repository of self patterns or self and non-self
patterns to train ALCs to be self tolerant
need to measure the affinity between an ALC and a pattern
To be able to measure affinity, the representation of the patterns
and the ALCs need to have the same structure
The affinity between two ALCs needs to be measured
memory that frequently detect non-self patterns
When an ALC detects non-self patterns, it can be cloned and the
clones can be mutated to have more diversity in the search
space
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AIS Framework
Algorithms
Affinity
Representation
Application
Solution
AIS
Shape-Space
Binary
Integer
Real-valued
Symbolic
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Representation – Shape Space
Used for modeling antibody and antigen
Determine a measure to calculate affinity
Hamming shape space(binary)
1 if Abi != Agi: 0 otherwise (XOR
operator)
Antibody
Antigen
0 0 1 1 0 0 1 1
1 1 1 0 1 1 0 1
Ab:
Ag:
1
0
1
0
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Representation
Assume the general case: Ab = Ab1, Ab2, ..., AbL Ag = Ag1, Ag2, ..., AgL
Binary representation
Matching by bits Continuous (numeric)
Real or Integer, typically Euclidian Symbolic (Categorical /nominal)
E.g female or male of the attribute Gender.
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AIS Framework
Algorithms
Affinity
Representation
Application
Solution
AISEuclidean
Manhattan
Hamming
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Affinity
Euclidean
Manhattan
Hamming
L
1i
2ii )Ag(AbD
L
iii AgAbD
1
L
i
ii AgAbD
1 otherwise0
if1δwhereδ,
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AIS Framework
Algorithms
Affinity
Representation
Application
Solution
AIS
Bone Marrow Models
Negative Selection
Clonal Selection
Positive Selection
Immune Network Models
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Basic AIS Algorithm
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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 Split into two parts: Censoring Monitoring
Selfstrings (S)
Generaterandom strings
(R0)Match Detector
Set (R)
Reject
No
Yes
No
Yes
Detector Set(R)
ProtectedStrings (S)
Match
Non-selfDetected
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All patterns and ALCs : as nominal valued attributes or as binary strings
Affinity : r-continuous matching rule
Training set : self patterns
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Training ALCs with negative selection
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Clonal Selection
Antigens
Proliferation Differentiation
Plasma cells
Memory cells
Selection
M
M
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Clonal Selection
selection of a set of ALCs with the highest calculated affinity with a non-self pattern
cloned and mutated
compete with the existing set of ALCs
to be exposed to the next non-self pattern Continuous (numeric)
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ClONALG algorithm
De Castro and Von presented CLONALG as an algorithm,2001
initially proposed to perform machine-learning pattern recognition
Adapted to be applied to optimization problem
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ClONALG algorithm
main immune aspects taken into account to develop the algorithm maintenance of a specific memory set selection and cloning of the most stimulated
Antibodies death of non-stimulated antibodies affinity maturation and re-selection of the
clones proportionally to their antigenic affinity generation and maintenance of diversity
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ClONALG
All patterns in binary strings
Training set : non-self patterns
Affinity : Hamming distance , between ALC and
non-self pattern
Lower Hamming distance = stronger affinity
Assumption : One memory ALC for each of the
patterns that needs to be recognized in training
set
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ClONALG
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CLONALG optimization case
an objective function g(⋅) must to be optimized
(maximized or minimized)
antibody affinity corresponds to the objective
function
each antibody Abi represents an element of the
input space
it is no longer necessary to maintain a separate
memory set Ab
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CLONALG optimization case
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CLONALG optimization case
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Immune Network Models
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 of an ALC based on the calculated affinity between the ALC
and the non-self pattern the calculated affinity between the ALC and
network ALCs as co-stimulation and/or co-suppression.
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Artificial Immune Network
Timmis and Neal,2000
Application clustering
data visualization
control
optimization domains
AINE defines the new concept of artificial recognition balls
(ARBs)
population of ARBs links between the ARBs a set of antigen training patterns Some clonal operations for learning
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Artificial Immune Network
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Artificial Immune Network
all training patterns in set DT are presented to
the set of ARBs
After each iteration, each ARB calculates its stimulation level Allocates resources (i.e. B-Cells) based on its
stimulation level as
The stimulation level antigen stimulation network stimulation network suppression
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Artificial Immune Network
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Stimulation level
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Resource allocation
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Danger Theory Models
distinguishes between
what is dangerous and
non-dangerous
Include a signal to
determine whether a
non-self pattern is
dangerous or not
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An Adaptive Mailbox
classifies interesting from uninteresting emails
initialization phase (training)
running phase (testing)
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initialization phase
running phase
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Application of AIS
network intrusion and anomaly detection
data classification models
virus detection
concept learning
data clustering
robotics
pattern recognition and data mining
optimization of multi-modal functions
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PSO and AIS
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PSO and AIS
PSO performs about 56 percent faster than.
AIS performs faster than PSO (about 14 percent)
for simpler mathematical functions
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Reference
Computational Intelligence An introduction ,
Adndries P.Engelbrecht
Learning and Optimization Using the Clonal
Selection Principle, Leandro N. de
Castro, ,Fernando J. Von Zuben, IEEE,2002
A Comparative Analysis on the Performance of
Particle Swarm Optimization and Artificial Immune
Systems for Mathematical Test Functions, 1David
F.W. Yap, 2S.P. Koh, 2S.K. Tiong,Australian Journal
of Basic and Applied Sciences, 2009
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