A Michigan-like immune-inspired framework for performing independent component analysis over Galois fields of prime order

In this work, we present a novel bioinspired framework for performing ICA over finite (Galois) fields of prime order P. The proposal is based on a state-of-the-art immune-inspired algorithm, the cob-aiNet[C], which is employed to solve a combinatorial optimization problem — associated with a minimal entropy configuration — adopting a Michigan-like population structure. The simulation results reveal that the strategy is capable of reaching a performance similar to that of standard methods for lower-dimensional instances with the advantage of also handling scenarios with an elevated number of sources. •We develop an immune-inspired framework for performing ICA over finite fields.•The framework addresses the problem as a population of distinct solutions that represent each extraction vector.•The proposal is implemented with the state-of-the-art cob-aiNet[C] algorithm.•The simulation results reveal that the method is competitive for lower-dimensional scenarios, while it also handles larger instances.