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Keywords Neural net, Genetic engineering, Artificial intelligence, Business support services, Decision making
Abstract Managing large amounts of information and efficiently using this information in improved decision making has become increasingly challenging as businesses collect terabytes of data. Intelligent solutions, based on neural networks (NNs) and genetic algorithms (GAs), to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. The current study provides an overview for the operations researcher of the neural networks and genetic algorithms methodology, as well as their historical and current use in business. The main aim is to present and focus on the wide range of business areas of NN and GA applications, avoiding an in-depth analysis of all the applications - with varying success recorded in the literature. This review reveals that, although still regarded as a novel methodology, NN and GA are shown to have matured to the point of offering real practical benefits in many of their applications.
1. Introduction
Applications of artificial intelligence (AI) techniques and modeling devices are attracting increasing interest in business literature. The main focus of attention so far has been expert systems (ES), which have been discussed by a number of authors (Wright and Rowe, 1992; Wong et al., 1994; Jayaraman and Srivastava, 1996; Metaxiotis et al, 2001; Metaxiotis and Psarras, 2003a). However, in the past decade there has also been a virtual explosion of interest in the fields of neural networks (NNs) and genetic algorithms (GAs). Appearing from seemingly out of nowhere, NNs and GAs have quickly evolved from an academic notion into proven and highly marketable products. They provide powerful and flexible means for obtaining solutions to a variety of problems that often cannot be dealt with by other, more traditional and orthodox methods. Nowadays their use is being proliferated to many sectors of our social life, while their applications are proved to be critical in the process of decision support and decision making.
This review bears witness to the enthusiastic application of NNs and GAs across a wide range of business-related problems with varying success. Certainly, this is not the first paper to review neural networks and genetic algorithms. The developments in these fields have been reviewed by several authors from various points of view...