states x i t the number of states x i t and the number of weighting factors is incremented with each iteration in order to have a consistent set of weighting factors the weighting factors are recalculated in each iteration for all respectively for a set of selected states by fitting u03c1 j in the state space it is possible to use all the information of formula and o for the states x i t generated so far the fitting step allows to use different fitting strategies for example the fitting can bias the sampling away from areas where intensive sampling has been done in the preceding iterations or the sampling can be directed along local gradients respectively towards local minima or maxima of one or several weighted properties in each of the iterations the sampling distribution function is fitted in a way to improve the overall sampling of the state space the method supports multi objective optimisations state space integrals can be solved it reduces the probability that the system is trapped the invention is general it can be used with different sampling methods in particular with monte carlo sampling metropolis monte carlo sampling or dynamic simulations it can be combined with the concepts of simulated annealing and multicanonical sampling it provides a general framework that can be adapted to the system and the observables of interest application_number US 52419106 A description exploration of the state space proceeds in a series of steps shown in fig1 in a first step 1 an initial sampling distribution function u03c1 1 x is selected if necessary other starting parameters are.