  Hedging in the presence of transaction costs leads to complex optimization
problems. These problems typically lack closed-form solutions, and their
implementation relies on numerical methods that provide hedging strategies for
specific parameter values. In this paper we use a genetic programming algorithm
to derive explicit formulas for near-optimal hedging strategies under nonlinear
transaction costs. The strategies are valid over a large range of parameter
values and require no information about the structure of the optimal hedging
strategy.
