  The representation used for solutions in optimization can have a significant
impact on the performance of the optimization method. Traditional population
based evolutionary methods have homogeneous populations where all solutions use
the same representation. If different representations are to be considered,
different runs are required to investigate the relative performance. In this
paper, we illustrate the use of a population based evolutionary method, Fresa,
inspired by the propagation of Strawberry plants, which allows for multiple
representations to co-exist in the population.
  Fresa is implemented in the Julia language. Julia provides dynamic typing and
multiple dispatch. In multiple dispatch, the function invoked is determined,
dynamically at run time, by the types of the arguments passed to it. This
enables a generic implementation of key steps in the plant propagation
algorithm which allows for a heterogeneous population. The search procedure
then leads to a competition between representations automatically.
  A simple case study from the design of operating conditions for a batch
reactor system is used to illustrate heterogeneous population based search.
