1. Statistical analysis of genetic algorithms in discovering technical trading strategies (S.H. Chen, C.Y. Tsao). 2. A genetic programming approach to model international short-term capital flow (T. Yu, S.H. Chen, T.W. Kuo). 3. Tools for non-linear time series forecasting in economics: An empirical comparison of regime switching vector autoregressive models and recurrent neural networks (J.M. Binner, T. Elger, B. Nilsson, J.A. Tepper). 4. Using non-parametric search algorithms to forecast daily excess stock returns (N.L. Joseph, D.S. Bree, E. Kalyvas). 5. Co-evolving neural networks with evolutionary strategies: A new application to Divisia Money (J. Binner, G. Kendall, A. Gazely). 6. Forecasting the EMU inflation rate: Linear econometric versus non-linear computational models using genetic neural fuzzy systems (S. Kooths, T. Mitze, E. Ringhut). 7. Finding or not finding rules in time series (J. Lin, E. Keogh). 8. A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil (S. Mirmirani, H.C. Li). 9. Searching for Divisia/Inflation Relationships with the aggregate feed forward neural network (V.A. Schmidt, J.M. Binner). 10. Predicting housing value: Genetic algorithm attribute selection and dependence modelling utilising the gamma test (I.D. Wilson, A. J. Jones, D.H. Jenkins, J.A. Ware).