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Artificial Immune Systems
Steve CayzerSemantic and Adaptive SystemsHewlett-Packard Laboratories,
Bristol
February 2006
page 3 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
The Immune System is…
Immunity: state or quality of being resistant (immune), either by virtue of previous exposure (adaptive immunity) or as an inherited trait (innate immunity)
Immune system: a system that protectsthe body from foreign substances and pathogenic organisms by producing the immune response
page 4 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
So for the purposes of this seminar…
• The human body is constantly under attack from pathogens which produce antigens (foreign proteins)
• The immune system (lymphocytes) recognise the antigens and cause the pathogens to be destroyed
• … without destroying the host (self proteins)
• Each lymphocyte matches a range of proteins: as a population, the immune system (learns to) cover non-self space.
• Adaptive, self organising system: good paradigm for ‘new’computing?
page 5 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Antibodies map non-self space
Self
X
Antibody (with recognition radius)
Non-Self
page 6 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Antibodies map non-self space
Self
Non-Self
X
page 7 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Antibodies map non-self space
Self
Autoreactive Antibody Destroyed
Non-Self
X
page 8 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Antibodies map non-self space
Self
Non-Self
XX
Antigen(matched by antibody)
page 9 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Antibodies map non-self space
Self
Non-Self
XX
Clonal MaturationWith hypermutation
XX
page 10 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
• basic model of an immune component (eg lymphocyte, often conflated with antibody)
• design informed from immunology• aimed at problem solving
Definition of AIS
de Castro and Timmis: “Artificial Immune Systems (AIS) are adaptive systems, inspired by theoretical immunology and observed immune functions, principles and models, which are applied to problem solving”
http://www.cs.kent.ac.uk/people/staff/jt6/aisbook/
page 11 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Models, Design Features, Applications
Models
l Negative Selection
l Positive Selection
l Danger model
l Self Assertion
Features
l Learning
l Associative Memory
l Avoids ‘self’
l Autonomous
Applications
l Security
l Classification/Clustering
l Optimisation
l Modelling
page 12 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Framework for AIS design
Application Domain
Representation
Affinity (cf fitness in GA)
Algorithms
page 13 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
GA – basic algorithm
Initialise population
WHILE (not finished)
Calculate fitnessSelectReproduceCrossover and mutationReplace
END WHILE
page 14 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – basic algorithm
Initialise population of lymphocytes
WHILE (not finished)
Calculate fitnessSelectReproduceCrossover and mutationReplace
END WHILE
page 15 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – basic algorithm
Initialise population of lymphocytes
WHILE (not finished)
Calculate fitnessSelectReproduceCrossover and mutationReplace
END WHILE
page 16 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – basic algorithm
Initialise population of lymphocytes
WHILE (not finished)
Present antigenCalculate fitnessSelectReproduceCrossover and mutationReplace
END WHILE
page 17 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – basic algorithm
Initialise population of lymphocytes
WHILE (not finished)
Present antigenCalculate fitness (=match to antigen)SelectReproduceCrossover and mutationReplace
END WHILE
page 18 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – basic algorithm
Initialise population of lymphocytes
WHILE (not finished)
Present antigenCalculate fitness (=match to antigen)SelectReproduceCrossover and mutationReplace
END WHILE
page 19 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – basic algorithm
Initialise population of lymphocytes
WHILE (not finished)
Present antigenCalculate fitness (=match to antigen)SelectReproduce (clonal expansion)Crossover and mutationReplace
END WHILE
page 20 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – basic algorithm
Initialise population of lymphocytes
WHILE (not finished)
Present antigenCalculate fitness (=match to antigen)SelectReproduce (clonal expansion)Mutation (no crossover)Replace
END WHILE
page 21 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – basic algorithm
Initialise population of lymphocytes
WHILE (not finished)
Present antigenCalculate fitness (=match to antigen)SelectReproduce (clonal expansion)Mutation (no crossover)Replace (variable population)
END WHILE
page 22 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – basic algorithm
Initialise population of lymphocytes
WHILE (not finished)
Present antigenCalculate fitness (=match to antigen)SelectReproduce (clonal expansion)Mutation (no crossover)Replace (variable population)Memory cells
END WHILE
page 23 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – basic algorithm
Initialise population of lymphocytes
WHILE (not finished)
Present antigenCalculate fitness (=match to antigen)SelectReproduce (clonal expansion)Mutation (no crossover)Replace (variable population)Memory cells
END WHILE
page 24 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Other notes
• Application areas– Classification– Clustering– Computer security
• Philosophical divide– GA or neural net- Or something else?
• New Approaches– Danger Theory – Gene Libraries
(end)
page 25 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Supervised Tasks: AIRS
AIS for classification and clustering (1)
Application Domain
Representation
Affinity (cf fitness in GA)
Algorithms K- Nearest Neighbour
Hamming (usu)
Varied (UCI datasets)
Data Mining
page 26 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIRS focus now shifting to parallel properties
AIS for classification and clustering (1)
82256
SonarDiabetesIonosphereIris
Watkins, A., Timmis, J., Boggess, L. 2004 “Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm”
Genetic Programming and Evolvable Machines 5 (3): 291-317
Supervised Tasks: AIRS
AIRS does competitively benchmarked against 35 classifiers on some standard datasets
(number represents ranking where 1 = best, ~35 = worst)
page 27 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Unsupervised Tasks: aiNET
AIS for classification and clustering (2)
Application Domain
Representation
Affinity (cf fitness in GA)
Algorithms Immune network model
Euclidean
Expression levels
Gene Expression Clustering
page 28 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Unsupervised Tasks: aiNet
•an iterative clustering algorithm that performs data compression using a pattern recognition process inspired by the human immune system.
•applied to a benchmark data set of gene expression levels
•Capable accurately detecting the presence of clusters without a priori knowledge about the number of clusters
AIS for classification and clustering (2)
Bezerra, G. B., de Castro, L. N. (2003), “A Hybrid Approach for Gene Expression Data Clustering”, International Conference on Bioinformatics and Computational Biology, 2003
http://www.vision.ime.usp.br/~cesar/programa/pdf/112.pdf
page 29 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Negative Selection: LISYS
AIS for Security (1)
Application Domain
Representation
Affinity (cf fitness in GA)
Algorithms Negative Selection
r contiguous bits
Network connections
Intrusion Detection
page 30 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
•LISYS does quite well on test data
•Problems with false positives?
•Does it scale?
AIS for Security (1)
S. A. Hofmeyr and S. Forrest (1999) “Immunity by Design: An Artificial Immune System”Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) pp. 1289-1296
Kim, J. and Bentley, P. J. (2001) "Evaluating Negative Selection in an Artificial Immune System for Network Intrusion Detection" , Genetic and Evolutionary Computation Conference 2001 (GECCO-2001) pp.1330 - 1337
Balthrop, J. Forrest, S. Glickman, M.R. (2002) “Revisiting LISYS: parameters and normal behavior” Proceedings of the 2002 Congress on Evolutionary Computation, CEC '02: 1045 - 50
page 31 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Problems with the self-nonself worldview
• How do we produce lymphocytes that react against antigens and yet avoid self?
• One way is “Generate and Test”: negatively screen lymphocytes which react to self at production time
• But this is expensive!
• It’s difficult to screen against ALL self.
• Self also changes over time
• And it is not necessary to screen against all non-self – only dangerous non-self
Aickelin & Cayzer 2002 The Danger Theory and Its Application to Artificial Immune Systems Proc. International Conference on AIS (ICARIS 2002)
page 32 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
The Danger Theory
• In the danger model, the idea is to recognise ‘danger’rather than non self.
• The screening is accomplished post production through an external ‘danger’ signal.
• Thus the production of autoreactive lymphocytes (which react to self) is allowed.
• If an (eg autoreactive) lymphocyte matches a stimulus in the absence of danger, it is removed.
• Thus harmless antigens are tolerated, and changing self accommodated.
Matzinger (2002) The Danger Model: A renewed sense of self Science 296: 301-304
page 33 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
EPSRC Adventure Fund 2004-2007: HP, UCL, UWE, Nottingham
Aickelin U, Bentley P, Cayzer S, Kim J and McLeod J (2003): 'Danger Theory: The Link between AIS and IDS?', Proceedings ICARIS-2003, 2nd International Conference on Artificial Immune Systems, LNCS 2787, pp 147-155
“The danger theory suggests that the immune system reacts to threats based on the correlation of various (danger) signals, providing a method of ‘grounding’ the immune response, i.e. linking it directly to the attacker.”
www.dangertheory.com
page 34 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Other ways of using danger
Danger = Crime, Antigen = Suspect
or...
Danger = Context ?
It could also be useful for data mining, where the ‘danger’signal is a proxy measure of interest
‘Danger Zone’ can be spatial or temporal
Andrew Secker, Alex Freitas, and Jon Timmis (2005) “Towards a danger theory inspired artificial immune system for web mining” in A Scime, editor, Web Mining: applications and techniques, pages 145-168 (Idea Group)
page 35 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Gene Libraries
2 broken assumptionsrandom creation
uniform antigens
Gene Libraries bias lymphocyte creation
Steve Cayzer, Jim Smith, James Marshall and Tim Kovocs (2005) “What have Gene Libraries done for AIS?” Proc. 4th International Conference on AIS (ICARIS 2005)
page 36 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
So what can they do for AIS?
Cayzer & Smith 2006 Gene Libraries: Coverage, efficiency and diversity. AISB'06: Adaptation in Artificial and Biological Systems April 3rd-6th 2006 University of Bristol, Bristol, England
•Increase coverage?
•yes (but can be expensive)
•Decrease cost?
•Yes (but can reduce diversity)
•Map antigens?
•Probably! (expts ongoing)
•Cope with dynamic (co evolving) pathogens?
•Tbd…
page 37 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Genetic algorithm • Creation (gene libraries)
• Emphasis on mutation
• Matching ~ fitness (?)
• Variable population size
Considerations
• Role of antigen
• Preservation of diversity
AIS can be thought of as a special case of:
Philosophical Divide
page 38 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Genetic algorithm • Creation (gene libraries)
• Emphasis on mutation
• Matching ~ fitness (?)
• Variable population size
Considerations
• Role of antigen
• Preservation of diversity
AIS can be thought of as a special case of:
AIS as optimiser
Philosophical Divide
page 39 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Neural network• Pattern classification
• Unsupervised learning
• Topographic mapping
• Variable network topology
Considerations
• Interpreting response
• Training regime
Genetic algorithm • Creation (gene libraries)
• Emphasis on mutation
• Matching ~ fitness (?)
• Variable population size
Considerations
• Role of antigen
• Preservation of diversity
AIS can be thought of as a special case of:
AIS as optimiser
Philosophical Divide
page 40 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Neural network• Pattern classification
• Unsupervised learning
• Topographic mapping
• Variable network topology
Considerations
• Interpreting response
• Training regime
Genetic algorithm • Creation (gene libraries)
• Emphasis on mutation
• Matching ~ fitness (?)
• Variable population size
Considerations
• Role of antigen
• Preservation of diversity
AIS can be thought of as a special case of:
AIS as optimiser AIS as classifier
Philosophical Divide
page 41 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
… Alternatively …
Application Areas of AIS: Past, Present and Future E. Hart and J.Timmis. International Conference on Artificial Immune Systems (ICARIS), Banf, Canada. LNCS 3627. pp. 483-497. C.Jacob Et. al. (Eds) 2005.
page 42 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
New Approaches
• Innate response (works for most animals!)– Towards a Conceptual Framework for Innate Immunity Jamie Twycross and Uwe Aickelin Proc. of the
4th International Conference on Artificial Immune Systems, Banff, Canada, 2005 – R. Germain. An innately interesting decade of research in immunology. Nature Medicine,
10(12):1307–1320, 2004.
• Neuro-endocrine-immune interactions– J. Timmis and M. Neal. Once more unto the breach: Towards artificial homeostasis. Recent
Developments in Biologically Inspired Computing, pages 340–365, 2005.
• Danger Theory– Aickelin U and Cayzer S (2002): The Danger Theory and Its Application to Artificial Immune Systems',
Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS-2002), pp 141-148, Canterbury, UK
• Self Assertion (homeostasis)– Inspiration for the Next Generation of Artificial Immune Systems . P. Andrews and J.Timmis.
International Conference on Artificial Immune Systems (ICARIS), Banf, Canada. LNCS 3627. pp.126-138 . C.Jacob Et. al. (Eds) 2005
page 43 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Or, maybe, the Immune System is…
• Francisco Varela: Homeostasis, self-develop an efficient communication pathway in order to create (assert) and maintain a coherent self
• John Stewart: rejection and memory are side effects of the homeostatic maintain.
Hugues Bersini: “While host defense is a critical function, it is hardly the only one of interest. Indeed the immune system might be regarded as primarily fulfilling an altogether different role…”
KNOW THYSELF? The Self Assertion View
Immune system only knows itself, no
recognition is at play
ftp://iridia.ulb.ac.be/pub/bersini/ImmunoSelf.pdf
page 45 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
AIS – Refined algorithm
Basic Matching Algorithml Population of B Cells (antibodies)
l Clonal expansion and hypermutation
Extensionsl Lifecycle events, screening (positive/negative selection)
l Other IS elements (T Cells, cytokines)
l Network interactions (idiotypic effects)
l Other – localization, self adaptation, population control
Choices l Genotype/Phenotype (Representation & Shape Space)
l Matching (Hamming, Euclidean, r-contiguous, other)
page 46 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
The Idiotypic Effect: Antibody-antibody interactions
Jerne’s Big Idea (1974)
Idiotype: specificity of antibody (epitopes to which it will bind)
Idiotope: An idiotypic epitope
Evidence: Antibodies produced againstantibodies of same species (cf individual)Antigen
P1
Idiotypic Set
I1
Anti-Idiotypic Set
-
P2 I2
Internal Image of Antigen
+
P3 I3
page 47 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
The Idiotypic Effect – Why do we care?
• Biological importance - ???
• Immunological models – Varela, Castellani
• Pattern recognition – Timmis & Hunt
• Non-stationary environments (idiotypic memory) –Gaspar & Collard
• Multimodal Optimisation – de Castro
• Recommendation communities – Cayzer & Aickelin
page 48 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
i
n
jjiji
N
j
N
jjiijjiji
i
xkyxmxxmkxxmc
ratedeath
recognisedantigens
recognisedamI
recognisedantibodies
cdtdx
211 1
1 −
+−=
−
+
−
=
∑∑ ∑== −
Modelling the Idiotypic Effect
• For N antibodies, n antigens.• xi is the concentration of antibody i• yi is the concentration of antigen I• c, k1 and k2 are scaling constants
• mij is a matching function
page 49 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
l Antibodies as:entire solutions
‘building blocks’
l Antigens as:objective functionsconstraintsfit/feasible solutionssolutions to subproblemsweight combinations spanning Pareto optimal front
l AIS usually hybridised with GA:Antibody selectionGene library creation
l Emergent fitness sharing (generalist/specialist)
AIS for Optimisation
page 50 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Evaluationl Applied to TSP (of course), job shop scheduling, time series prediction, truss design, capacitor placement, time dependent optimization…
l Some good results on test problems
BUT…
l Often little ‘added value’ to GA
l AIS metaphor somewhat strained
l Difficult to find fair comparisons
l Best viewed as a collection of hybridising techniques
AIS for Optimisation
page 51 of 25 3/1/2006 Immune Systems - an evolutionary metaphor
Example: Hajela & Yoo 2001
Crossover
Mutationbest
Antigen
Designs
Generalist AIS
Antibody
GA(unconstrained objective function)
Feasible Infeasible
allSmall s