  Individual-based models are complex and they have usually an elevated number
of input parameters which must be tuned for reproducing the observed population
data or the experimental results as accurately as possible. Thus, one of the
weakest points of this modelling approach lies on the fact that rarely the
modeler has the enough information about the correct values or even the
acceptable range for the input parameters. Consequently, several parameter
combinations must be tried to find an acceptable set of input factors
minimizing the deviations of simulated and the reference dataset. In practice,
most of times, it is computationally unfeasible to traverse the complete search
space trying all every possible combination to find the best of set of
parameters. That is precisely an instance of a combinatorial problem which is
suitable for being solved by metaheuristics and evolutionary computation
techniques. In this work, we introduce EvoPER, an R package for simplifying the
parameter estimation using evolutionary computation methods.
