recursive least squares and quadratic prediction in continuous multistep problems
XCS with computed prediction, namely XCSF, has been recently extended in several ways. In particular, a novel prediction update algorithm based on recursive least squares and the extension to polynomial prediction led to significant improvements of XCSF. However, these extensions have been studied so far only on single step problems and it is currently not clear if these findings might be extended also to multistep problems. In this paper we investigate this issue by analyzing the performance of XCSF with recursive least squares and with quadratic prediction on continuous multistep problems. Our results show that both these extensions improve the convergence speed of XCSF toward an optimal performance. As showed by the analysis reported in this paper, these improvements are due to the capabilities of recursive least squares and of polynomial prediction to provide a more accurate approximation of the problem value function after the first few learning problems.
recursive least squares multistep problems learning classifier systems