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

Chapter 5. EA Based Model — COSDM 13244Defined length: 8 bits53Defined length: 32 bits4352Mean recall accuracy42414039381 schema replaced5 schemas replacedMean recall accuracy51504948471 schema replaced5 schemas replaced3746360 100 200 300 400 500 600 700Generation450 100 200 300 400 500 600 700GenerationFigure 5.7: Output from experiments in which datasets generated from 5 schema wereupdated at intervals of 50 generations by replacing g schema g s, the resulting value of r immediately following the antigen update is still verymuch greater that the fitness observed at generation 0 when the system is randomlyinitialised. A clearer indication of the success of the model in adapting to the new datais shown by examining the difference ∆ between the maximum value achieved at theend of the tolerization period r £ U 0 ¥ (generation t=200) and the maximum value of rachieved following each subsequent update, r £ U i ¥ , i.e. ∆ r £ U 0 ¥ r £ U i ¥ . Figure 5.9illustrates representative results, showing the value of ∆ for the extreme values of g inexperiments in which d 16 and s 5¤ 10. Although the values of ∆ fluctuate aboveand below the zero-line, the magnitude of the deviations is very small compared to theactual value of r achieved at the end of the comparatively long tolerization period:- themaximum negative deviation is 3¦ 43% of r £ U 0 ¥ , and the maximum positive deviation5¦ 29% of r £ U 0 ¥ across all experiments. Therefore we tentatively conclude that theCOSDM model exhibits some ability to track moving datasets.5.6 Experiments using Cycling Non-StationaryDatasetsEarlier discussion of the properties of the biological immune system suggested thatthe basis of learning in such systems is attributable to its long-term memory capacity,which enables it to respond more rapidly and more effectively to subsequent encounterswith antigens. Therefore, it is essential to investigate whether the proposed arti-