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A Matlab Tour on Some AIS Algorithms BIC 2005: International Symposium on Bio-Inspired Computing Johor, MY, 10 th September 2005 Dr. Leandro Nunes de Castro [email_address] http://lsin.unisantos.b/lnunes Catholic University of Santos - UniSantos/Brazil
1) Generate a set (P) of candidate solutions, composed of the subset of memory cells (M) added to the remaining (P r ) population (P = P r + M)
2) Determine the n best individuals of the population (P n ), based on an affinity measure
3) Clone (reproduce) these n best individuals of the population, giving rise to a temporary population of clones ( C ). The clone size is an increasing function of the affinity with the antigen;
4) Submit the population of clones to a hypermutation scheme, where the hypermutation is proportional to the affinity of the antibody with the antigen. A maturated antibody population is generated ( C* );
5) Re-select the improved individuals from C* to compose the memory set. Some members of the P set can be replaced by other improved members of C* ;
6) Replace d low affinity antibodies of the population, maintaining its diversity.
The evolutionary artificial immune network, named aiNet, is an edge-weighted graph , not necessarily fully connected, composed of a set of nodes, called cells , and sets of node pairs called edges with a number assigned called weight , or connection strength , specified to each connected edge.
1. Randomly initialize a population of cells (initial number not relevant)
2. While not [constant memory population], do
2.1 Calculate the fitness and normalize the vector of fitnesses.
2.2 Generate a number Nc of clones for each network cell.
2.3 Mutate each clone proportionally to the fitness of its parent cell, but keep the parent cell.
2.4 Determine the fitness of all individuals of the population.
2.5 For each clone, select the cell with highest fitness and calculate the average fitness of the selected population.
2.6 If the average fitness of the population is not significantly different from the previous iteration, then continue. Else , return to step 2.1
2.7 Determine the affinity of all cells in the network. Suppress all but the highest fitness of those cells whose affinities are less than the suppression threshold s and determine the number of network cells, named memory cells, after suppression.
2.8 Introduce a percentage d % of randomly generated cells and return to step 2.
encoding, static population size, no inter-cell interaction, different mutation scheme
equal to ( + )-ES, where = N and = Nc; both use Gaussian mutation, but with different standard deviations, static population size, no diversity introduction, no direct interaction within the population