B Cell–Inspired Algorithms - Immunological Computation: Theory and Applications - page 134

original opt-aiNet()

1. Initialization

Create a random initial population of B cells (initial immune network)

2. Immune Network Dynamics

while not stopping criterion met do

2.1. compute B cell fitness

for each B cell do

compute the fitness of current B cell

normalize vector of all B cell fitnesses

2.2. clonal expansion

for each B cell do

generate N c clones of current B cell
add new clones to current B cell population

2.3. somatic mutation

for each B cell clone do

mutate each clone proportionally to the parent B cell's fitness

2.4. fitness re-evaluation

for each B cell do

compute fitness of current B cell

2.5. clonal selection

select fittest clones and discard clones with the lowest fitness

2.6. compute average fitness

2.7. network supression

suppress B cells whose affinities are below the suppression

threshold s

2.8. memory cell differentiation

determine memory cells after suppression.

2.9. metadynamics

introduce a percentage d of new randomly generated B cells into the network

Figure 5.8 The orginal opt-aiNet algorithm.