Universidad Carlos III de Madrid - UC3M

The use of soft computing in finance and economics has become increasingly important. These methods have been proven to be a powerful tool in domains were analytic solutions are not a good alternative. Real-world problems involve complexity, noisy environments, uncertainty and vagueness, hence the popularity of this approach among both researchers and practitioners. So far it has been successfully used in financial engineering, risk management, portfolio optimization, industrial organization, auctions, searching equilibriums, studying the emergence of the representative agent and rational expectations, designing public policy, financial forecasting, market simulation or agent-based computational economics, among many other areas.

The use of heuristics has been proven to be a powerful approach in domains were analytic solutions are not a good alternative. Real-world financial and economic problems involve complexity, noisy environments, uncertainty and vagueness, hence the popularity of this approach among both researchers and practitioners. So far it has been successfully used in financial engineering, risk management, portfolio optimization, designing public policy, financial forecasting, or agent-based computational economics, among many other areas.

In this forthcoming WCCI’2012, the Computational Finance and Economics Technical Committee (CFETC with its six constituents (task forces) will jointly organize a special session on “Computational Intelligence in Finance, Economics and Management Sciences’’ (CIFEMS). The aim of the event is to facilitate, stimulate, and inspire dialogue among researchers and practitioners working on this area

Multiobjective optimization arises in many real-world engineering applications, in which several optimization criteria conflict with each other. Since no single solution can optimize all the objectives simultaneously, metaheuristics are designed to find a set of solutions that help engineers to choose the optimal trade-off according to their needs. Besides, solving real-world problems faces metaheuristic algorithms to additional challenging tasks, such as high computational requirements, hardly constrained, high dimensionality, etc. These challenges have to be addressed properly in order to provide this kind of problems with high quality solutions. Multiobjective optimization has therefore attracted growing attention and become one of the hottest research topics in the area of the metaheuristics.