Traditional parametric methods have limited success in estimating and forecasting the volatility of financial securities. Recent advance in evolutionary computation has provided additional tools to conduct data mining effectively. The current work applies the genetic programming in a Time Series Data Mining framework to characterize the S&P100 high frequency data in order to forecast the one step ahead integrated volatility. Results of the experiment have shown to be superior to those derived by the traditional methods.