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

Chapter 3. Immune Systems for Scheduling 62that binary immune systems evolve faster and end up with higher fitness values as theantigen exposure rate is increased. A possible explanation for this is that the ’optimal’schedules corresponding to each of the antigens in the universe are so diversethat especially at low N the expressed antibody repertoire is too general to providereasonable solutions. Furthermore, in a binary system, there is a higher probability ofa completely random antibody matching any given antigen than in the integer-basedsystem modelled here.Figure 3.5 (c) replicates the findings of [Perelson et al., 1996], in that increasingthe number of points in the genome which are mutated improves fitness sharply over anarrow range of values of M, as a result of the Baldwin effect. Figure 3.5 (d), whichcompares the evolving fitness of the immune system with increasing generations forexperiments that do and do not include somatic mutation, shows that including somaticmutation results in a more rapid evolution, and results in a higher overall fitness, again,as predicted in [Perelson et al., 1996].Therefore, we conclude that the integer-based model with the interaction formedby the Ag-Ab complex describing a schedule is suitable for evolving immune librariesfrom which schedules can be generated. Best results will be achieved using low valuesof K, the antigen exposure rate, and high values of N, the antibody expression rate,and an element of somatic mutation. Given the time constraints introduced when usinglarge values of N, we elected to set N 0¦ 005 in remaining experiments described, asevolution still occurs even at this level. Unless otherwise stated, the antigen exposurerate was set to 4.3.7 Evaluation of the Immune ResponseThe experimental procedure adopted for evaluation of the libraries evolved using thegenetic algorithm consisted of three distinct phases:1. Measure the quality of the schedules produced from evolved libraries in responseto scenarios described in the AU the library was evolved in.2. Measure the quality of the schedules produced from evolved libraries in responseto previously unseen scenarios, i.e. generate a set of new antigen universes