Genetic programming with Monte Carlo simulation for option pricing

I examine the role of programming parameters in determining the accuracy of genetic programming for option pricing. I use Monte Carlo simulations to generate stock and option price data needed to develop a genetic option pricing program. I simulate data for two different stock price processes - a geometric Brownian process and a jump-diffusion process. In the jump-diffusion setting, I seed the genetic program with the Black-Scholes equation as a starting approximation. I find that population size, fitness criteria, and the ability to seed the program with known analytical equations, are important determinants of the efficiency of genetic programming.