Enabling XCSF to cope with dynamic environments via an adaptive error threshold
Tim Hansmeier, Paul Kaufmann, Marco Platzner · 2020
The learning classifier system XCSF is a variant of XCS employed for function approximation. Although XCSF is a promising candidate for deployment in autonomous systems, its parameter dependability imposes a significant hurdle, as a-priori parameter optimization is not feasible for complex and changing environmental conditions. One of the most important parameters is the error threshold, which can be interpreted as a target bound on the approximation error and has to be set according to the approximated function. To enable XCSF to reliably approximate functions that change during runtime, we propose the use of an error threshold, which is adapted at run-time based on the currently achieved approximation error. We show that XCSF with an adaptive error threshold achieves superior results over static thresholds in dynamic scenarios, where in general there exists no one-fits-all static threshold.