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

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Chapter 5. EA Based Model — COSDM 139order to progress this work, two features of the COSDM model needed to be addressed,namely that of the engine by which the correct locations of the hard antibodies couldbe found, and a method to circumvent the difficulties associated with the recognitionradii. The next chapter presents a new model — SOSDM — which borrow from amodifed version of Kanerva’s SDM suggested by [Hely et al., 1997] and disregardsthe EA in favour of a self-organising metaphor more in keeping with the principles ofthe immune system we are attempting to mimic.
Chapter 6A Self-Organising SDM — SOSDM6.1 IntroductionThe previous chapter concluded that the combination of an EA with an immune systemmetaphor suffered from three major drawbacks; namely, that the evolved immunesystems failed to recognise some antigen altogether, evolving the correct recognitionradii for each antibody was extremely difficult, and that the system was relatively slowto evolve, owing to the nature of the fitness function. In this chapter, another methodof discovering an SDM immune system capable of adapting to non-stationary data isdescribed. The new model, self-organising SDM or SOSDM, relies on an importantprinciple of the biological immune system not yet explored in this thesis — its selforganisingnature.As previously discussed, the original form of the SDM is essentially a staticmemory, with fixed hard locations. COSDM represented hard locations by antibodies,and attempted to evolve the best definition of those antibodies, subject tothe current state of the environment using an evolutionary algorithm. However,SOSDM views the memory as a truly self-organising system. Initially randomlyplaced antibodies self-organise in order that they become distributed throughout theinput antigen data space in a manner which reflects the input antigen data distribution.This seems an entirely logical step — the immune system itself is selforganising,whilst viewed from the computational angle, there is an abundance of140
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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 13antigenepit
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Chapter 2. Background 17Antigene 0p
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Chapter 2. Background 19maintained
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Chapter 2. Background 23genetically
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Chapter 2. Background 251. Choose a
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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 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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