A stock selection model with both discrete and continuous decision variables is proposed, in which a novel sigmoid-based mixed discrete-continuous differential evolution algorithm is especially developed for model optimization. In particular, a stock scoring mechanism is first designed to evaluate candidate stocks based on their fundamental and technical features, and the top-ranked stocks are selected to formulate an equal-weighted portfolio. Generally, the proposed model makes literature contributions from two main perspectives. First, to determine the optimal solution in terms of feature selections (discrete variables) and the corresponding weights (continuous variables), the original differential evolution algorithm focusing only on continuous problems is extended to a novel mixed discrete-continuous variant based on sigmoid-based conversion for the discrete part. Second, the stock selection model also resolves the gap of the application of differential evolution algorithm to stock selection. Using the Shanghai A share market of China as the study sample, the empirical results show that the novel stock selection model can make a profitable portfolio and significantly outperform its benchmarks (with other model designs and optimization algorithms used in the existing studies) in terms of both investment return and model robustness.

With the development of social economy, the increasing demand on hazardous materials transportation has received considerable attention around the world and the location-routing problem has become a hot topic of study. The goal of our work is to obtain the best balance between the transportation risk and cost under the assumptions that the transportation costs and the number of affected people are fuzzy variables. Within the framework of credibility theory, we propose a chance-constrained programming model and design a fuzzy simulation–based genetic algorithm to solve the model. Numerical experiments are given to illustrate the efficiency of the proposed model and algorithm.

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back-propagation neural network (BPNN)-based forex forecasting subsystem and Web-based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e-service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.

Data preparation is an important and critical step in neural network modeling for complex data analysis and it has a huge impact on the success of a wide variety of complex data analysis tasks, such as data mining and knowledge discovery. Although data preparation in neural network data analysis is important, some existing literature about the neural network data preparation are scattered, and there is no systematic study about data preparation for neural network data analysis. In this study, we first propose an integrated data preparation scheme as a systematic study for neural network data analysis. In the integrated scheme, a survey of data preparation, focusing on problems with the data and corresponding processing techniques, is then provided. Meantime, some intelligent data preparation solution to some important issues and dilemmas with the integrated scheme are discussed in detail. Subsequently, a cost-benefit analysis framework for this integrated scheme is presented to analyze the effect of data preparation on complex data analysis. Finally, a typical example of complex data analysis from the financial domain is provided in order to show the application of data preparation techniques and to demonstrate the impact of data preparation on complex data analysis.