This paper proposes a non-parametric hybrid system for autonomous navigation combining the strengths of learning classifier systems, evolutionary algorithms, and an immune network model. The system proposed is basically an immune network of classifiers, named CLARINET. CLARINET has three degrees of freedom: the attributes that define the network cells (classifiers) are dynamically adjusted to a changing environment; the network connections are evolved using an evolutionary algorithm; and the concentration of network nodes is varied following a continuous dynamic model of an immune network. CLARINET is described in detail, and the resultant hybrid system demonstrated effectiveness and robustness in the experiments performed, involving the computational simulation of robotic autonomous navigation.
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