Immunology as a Metaphor for Computational ... - Napier University

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Chapter 5. EA Based Model — COSDM 117SDMDatabasestore/retrieve dataspecies representative111110000 29location radiusfitnessform "serum" consistingof one representative fromeach speciesspecies/populationbeing evaluatedspecies 1EAspecies 2EAspecies 3EAbest memberpopulationpopulationpopulationFigure 5.2: Coevolutionary architecture of the COSDM modelother population. Credit is then assigned back to the population under evaluation, andthe usual mechanisms of selection and reproduction then take place within each population.In the implementation of the model described, populations are evaluated in aserial fashion, but there is no intrinsic barrier to performing a parallel evaluation.Potter points out that there are four issues to be addressed in trying to produce anevolving computational model that provides reasonable opportunities for emergenceof coadapted subcomponents of a larger problem. The properties of the proposed architecturethat are desirable in the context of evolving an immune system for datarecognition are now discussed. For a detailed explanation of other characteristics ofthe architecture, the reader is referred to the original paper, [Potter and De Jong, 2000].Problem Decomposition When searching for the optimal immune system to store alarge dataset, it is impossible to know a priori how many antibodies are required,particularly if the data is non-stationary. Therefore, the model should allow foraddition and deletion of antibodies as the adaption takes place. This is simple to
Chapter 5. EA Based Model — COSDM 118achieve in the CE-POTTER architecture by adapting the mechanisms for addingand deleting subcomponents. The method by which this is implemented is describedfurther in section 5.2.2.Interdependent subcomponents As already identified above, the antibodies will exhibita high degree of interdependency — COSDM is a distributed system inwhich antigen bind to many antibodies during both the storage and retrievalphases. Therefore, an antibody cannot be evolved in isolation. This kind of interdependentrelationship is also observed in the biological immune system, wherethe specificity of an anti-serum is a function of a number of interacting antibodiesand not a result of a single antibody reacting exclusively with the inducingantigen [Roitt et al., 1988]. The architecture described handles interdependentcomponents as the evaluation phase evaluates one species in the context of allthe other species by forming the immune system — it is impossible to evaluatea species in isolation.Credit assignment When tackling problems that have have been broken down intosubcomponents, the issue of distributing credit to each of the components alwaysarises. The mechanism described by Potter which is used in order to determinethe fitness of members of one species is to evaluate those individualsin conjunction with the best members of each of the other species, i.e. the individualscontributing to the serum from the other species remain fixed. Theresulting fitness of the entire serum is then assigned to the individual being evaluatedalone. This is particularly applicable to credit assignment for the immunesystem, where it would be very difficult to determine exactly how much a singleantibody contributed to the overall fitness of the immune system.Diversity Clearly, the repeated application of an EA to a population will eventuallyresult in the convergence of that population. As fitness in a population is a resultof collaboration and cooperation between all subcomponents, the architecturedescribed should enable sufficient diversity to be maintained in each species untilthe complete problem has been solved. Furthermore, in a non-stationary system,there is a requirement to maintain sufficient diversity within the populations to
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AbstractThe biological immune syste
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DeclarationI declare that this thes
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Table of Contents1 Introduction 11.
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5.3.1 Default Parameters . . . . .
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List of Figures2.1 ’Lock and Key
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List of Tables2.1 Structural and fu
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Chapter 1IntroductionThe study of b
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Chapter 1. Introduction 3scientists
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Chapter 1. Introduction 5Thus, the
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Chapter 1. Introduction 7of the cha
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Chapter 1. Introduction 94. Analysi
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Chapter 2. Background 11paratopeepi
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Chapter 2. Background 23genetically
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Chapter 2. Background 27non-interbr
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Chapter 2. Background 29been made i
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Chapter 2. Background 31patterns wi
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Chapter 2. Background 33reported on
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Chapter 2. Background 35Immunos 81
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Chapter 2. Background 37recognition
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Chapter 2. Background 391. Create a
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Chapter 2. Background 41environment
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Chapter 2. Background 43a)input dat
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Chapter 2. Background 45antibody wi
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Chapter 3. Immune Systems for Sched
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Chapter 6. A Self-Organising SDM
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Chapter 7Conclusion7.1 OverviewThis
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Chapter 7. Conclusion 181however wh
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Chapter 7. Conclusion 183features m
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Chapter 7. Conclusion 185In additio
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Chapter 7. Conclusion 187ter, sched
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Chapter 7. Conclusion 189Clustering
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Chapter 7. Conclusion 191nature in
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Appendix ACoincidences in permutati
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Appendix BExperimental results obta
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Appendix B. Experimental results ob
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Bibliography 203[Cooke and Hunt, 19
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Bibliography 205[Farmer et al., 198
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Bibliography 207[Hart et al., 1998]
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Bibliography 209[Kohonen, 1982b] Ko
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Bibliography 211[Potter and De Jong
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Bibliography 213[Timmis et al., 199
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