Evolutionary Computation in Economics and Finance

Over the last few years, evolutionary computation gradually has played an active increasingly role in computational economics and finance. Evolutionary computation tools such as genetic algorithms, genetic programming, evolutionary programming, and evolutionary artificial neural nets have been extensively applied to forecasting financial time series, estimating econometric parameters, pricing options, replicating laboratory results with human subjects, selecting equilibria, studying the emergence of the representative agent and rational expectations, simulating artificial stock markets and social processes, explaining the stylized facts observed in financial markets, designing public policy, and so on.

In this special session, we would like to solicit the most recent advancement in this research area. Papers addressing novel applications of GAs, GP, EP, EANN,... to economics, game theory and finance are all welcome. Interested participants should send the title and abstract of the paper to Shu-Heng Chen no later than Jan. 15.

Papers presented at this special session will be referred for possible inclusion in a volume entitled "Evolutionary Computation in Economics and Finance" to be published by Springer-Verlag.

Organizers:
Shu-Heng Chen, Chia-Husan Yeh
AI-ECON Research Group
Department of Economics
National Chengchi University
Taipei, Taiwan 11623
http://econo.nccu.edu.tw/ai/groupai.htm