  In this paper we present an evolutionary optimization approach to solve the
risk parity portfolio selection problem. While there exist convex optimization
approaches to solve this problem when long-only portfolios are considered, the
optimization problem becomes non-trivial in the long-short case. To solve this
problem, we propose a genetic algorithm as well as a local search heuristic.
This algorithmic framework is able to compute solutions successfully. Numerical
results using real-world data substantiate the practicability of the approach
presented in this paper.
