  Kelly's Criterion is well known among gamblers and investors as a method for
maximizing the returns one would expect to observe over long periods of betting
or investing. These ideas are conspicuously absent from portfolio optimization
problems in the financial and automation literature. This paper will show how
Kelly's Criterion can be incorporated into standard portfolio optimization
models. The model developed here combines risk and return into a single
objective function by incorporating a risk parameter. This model is then solved
for a portfolio of 10 stocks from a major stock exchange using a differential
evolution algorithm. Monte Carlo calculations are used to verify the accuracy
of the results obtained from differential evolution. The results show that
evolutionary algorithms can be successfully applied to solve a portfolio
optimization problem where returns are calculated by applying Kelly's Criterion
to each of the assets in the portfolio.
