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

More documents

Recommendations

Info

Chapter 5. EA Based Model — COSDM 135Mean recall accuracy4443.54342.54241.541AAA B ABA BBCycling schema setsRandomly generated schema40.5200 250 300 350 400 450 500 550 600 650 700GenerationFigure 5.10: The figure compares the performance of COSDM on datasets generatedfrom random schemas, to datasets generated from two schema sets A, B which arere-introduced at regular intervals. The datasets in each case are generated from 5schema, each schema containing 8 defined bits.COSDM in which the entire antigen set is updated from randomly generated schemaat each update, rather than the alternating schema set just described. The figure clearlyshows that COSDM applied to the cycling data set produces better results than whenapplied to random moving data sets, suggesting that some kind of memory effect isbeing observed. (As in previous experiments a tolerization period of 200 generationswas applied in order to allow the system to learn the initial dataset.)In order to investigate the period of this memory, i.e. the length of the intervalsbetween re-introduction of familiar clusters, we repeated the above experiments settingk to 3, and then 4. We then examined the best fitness achieved for schema set A ineach case during every interval for which COSDM was exposed to this schema set.These values were averaged, and the mean and standard deviation calculated for eachexperiment defined by s¤ k¥ . The results are given in table 5.4.There is little observable difference between the mean values of r obtained as k£varies, and Students t-tests applied with 95% confidence limits show no significantdifferences. Therefore, it appears that the COSDM model is able to exhibit some formof memory for past clusters, and for the values of k tried, the extent of this memory is
Chapter 5. EA Based Model — COSDM 136Number of schemasValue of ks 2 3 42 44.63 (0.186) 44.37 (0.274) 44.77 (0.322)5 43.06 (0.382) 44.21 (0.342) 43.01 (0.405)10 42.72 (0.266) 42.78 (0.474) 42.79 (0.281)Table 5.4: The mean and standard deviation (shown in brackets) of the maximum valueof r found for schema set A, for varying combinations of s, the number of schemas fromwhich the dataset is generated, and k, the multiplier producing the overall schema setnot affected by the size of the intervals between reappearance of clusters.5.7 ConclusionsA new algorithm, COSDM, has been introduced which uses a genetic algorithm toevolve the position and radii of the hard locations in a sparse distributed memory. Itwas postulated at the beginning of this chapter that this would allow clusters in large,binary datasets to be identified. A co-evolutionary architecture was used, which it washoped would provide a means of automatically determining the number of clusters inthe data, when this is unknown a priori. Furthermore, the architecture was designedto enable the algorithm to be used in a non-stationary environment, so that it couldtheoretically track moving clusters.The experiments described show that the algorithm shows limited success. Firstconsider its performance in static environments (although the arguments now presentedapply equally to the non-static experiments also) — the experiments detailed in section5.4 show that although datasets containing 2 clusters are clustered more accuratelythan when using the CE-POTTER algorithm, performance of COSDM degrades as thenumber of clusters and the size of the datasets increases. Figure 5.6 may explain this;COSDM fails to recognise increasing numbers of antigens as the size of the datasetsize and number of clusters increases. The fitness function, i.e. r rewards memorieswhich on average are able to recall data more accurately; however, there is no driv-
Page 2 and 3:
AbstractThe biological immune syste
Page 4 and 5:
DeclarationI declare that this thes
Page 6:
Table of Contents1 Introduction 11.
Page 9 and 10:
5.3.1 Default Parameters . . . . .
Page 11:
List of Figures2.1 ’Lock and Key
Page 14 and 15:
List of Tables2.1 Structural and fu
Page 16 and 17:
Chapter 1IntroductionThe study of b
Page 18 and 19:
Chapter 1. Introduction 3scientists
Page 20 and 21:
Chapter 1. Introduction 5Thus, the
Page 22 and 23:
Chapter 1. Introduction 7of the cha
Page 24 and 25:
Chapter 1. Introduction 94. Analysi
Page 26 and 27:
Chapter 2. Background 11paratopeepi
Page 28 and 29:
Chapter 2. Background 13antigenepit
Page 30 and 31:
£Chapter 2. Background 15low the m
Page 32 and 33:
Chapter 2. Background 17Antigene 0p
Page 34 and 35:
Chapter 2. Background 19maintained
Page 36 and 37:
E £ ρ¥ £Chapter 2. Background 2
Page 38 and 39:
Chapter 2. Background 23genetically
Page 40 and 41:
Chapter 2. Background 251. Choose a
Page 42 and 43:
Chapter 2. Background 27non-interbr
Page 44 and 45:
Chapter 2. Background 29been made i
Page 46 and 47:
Chapter 2. Background 31patterns wi
Page 48 and 49:
Chapter 2. Background 33reported on
Page 50 and 51:
Chapter 2. Background 35Immunos 81
Page 52 and 53:
Chapter 2. Background 37recognition
Page 54 and 55:
Chapter 2. Background 391. Create a
Page 56 and 57:
Chapter 2. Background 41environment
Page 58 and 59:
Chapter 2. Background 43a)input dat
Page 60 and 61:
Chapter 2. Background 45antibody wi
Page 62 and 63:
Chapter 3. Immune Systems for Sched
Page 64 and 65:
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
££Chapter 3. Immune Systems for S
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101: Chapter 3. Immune Systems for Sched
Page 118 and 119: Chapter 4Applying an Immune System
Page 120 and 121: Chapter 4. Applying an Immune Syste
Page 130 and 131: Chapter 5. EA Based Model — COSDM
Page 136 and 137: JH £ c i ¤ a¥ :Chapter 5. EA Bas
Page 156 and 157: Chapter 6. A Self-Organising SDM
Page 166 and 167: J:¡Chapter 6. A Self-Organising SD
Page 168 and 169: ¡¡¡Chapter 6. A Self-Organising
Page 176 and 177: £Chapter 6. A Self-Organising SDM
Page 178 and 179: g£££1 At iteration 0 of the algo
Page 194 and 195: Chapter 7Conclusion7.1 OverviewThis
Page 196 and 197: Chapter 7. Conclusion 181however wh
Page 198 and 199: Chapter 7. Conclusion 183features m
Page 200 and 201:
Chapter 7. Conclusion 185In additio
Page 202 and 203:
Chapter 7. Conclusion 187ter, sched
Page 204 and 205:
Chapter 7. Conclusion 189Clustering
Page 206 and 207:
Chapter 7. Conclusion 191nature in
Page 208 and 209:
Appendix ACoincidences in permutati
Page 210 and 211:
Appendix BExperimental results obta
Page 212 and 213:
Appendix B. Experimental results ob
Page 214 and 215:
Page 216 and 217:
Page 218 and 219:
Bibliography 203[Cooke and Hunt, 19
Page 220 and 221:
Bibliography 205[Farmer et al., 198
Page 222 and 223:
Bibliography 207[Hart et al., 1998]
Page 224 and 225:
Bibliography 209[Kohonen, 1982b] Ko
Page 226 and 227:
Bibliography 211[Potter and De Jong
Page 228:
Bibliography 213[Timmis et al., 199
show all

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

Create successful ePaper yourself

Delete template?

Save as template?