Immune algorithm with immune network and MHC for adaptive problem solving

Adaptive problem solving techniques such as neural networks and genetic algorithms become so popular in the AI field. The biological immune system is one of the adaptive biological systems whose functions are to identify and to eliminate foreign materials. In this paper, we propose an adaptive optimization algorithm based on immune model with immune network and major histocompatibility complex (MHC). In biological immune system, immune network controls immune responses by changing its structure. The MHC is used to distinguish a "self" from other "not self". In our model, immune network is used to produce adaptive behaviors of agents, which are computing subject for problem solving. MHC is used to induce competitive behaviors among agents. To investigate an adaptation ability of the proposed algorithm, we apply it to the n-th agent's travelling salesman problem called n-TSP. This algorithm performs adaptive behaviors for distributed cooperation.