We show the experimental setup and the simulation
results, including the out-of-sample performance of the optimal
tracking portfolio.
The hybrid genetic algorithms used appear
to be robust in finding the optimal tracking portfolio and
the performance of this portfolio on the out-of-sample data
set is approximately four times better than that of randomly
selected portfolios with optimized stock weights.