Kelly's Criterion is well known among gamblers and investors as a method formaximizing the returns one would expect to observe over long periods of bettingor investing. These ideas are conspicuously absent from portfolio optimizationproblems in the financial and automation literature. This paper will show howKelly's Criterion can be incorporated into standard portfolio optimizationmodels. The model developed here combines risk and return into a singleobjective function by incorporating a risk parameter. This model is then solvedfor a portfolio of 10 stocks from a major stock exchange using a differentialevolution algorithm. Monte Carlo calculations are used to verify the accuracyof the results obtained from differential evolution. The results show thatevolutionary algorithms can be successfully applied to solve a portfoliooptimization problem where returns are calculated by applying Kelly's Criterionto each of the assets in the portfolio.
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