The merging of neural networks, fuzzy logic, and genetic algorithms

During the last decade, there has been increased use of neural networks (NNs), fuzzy logic (FL) and genetic algorithms (GAs) in insurance-related applications. However, the focus often has been on a single technology heuristically adapted to a problem. While this approach has been productive, it may have been sub-optimal, in the sense that studies may have been constrained by the limitations of the technology and opportunities may have been missed to take advantage of the synergies between the technologies. For example, while NNs have the positive attributes of adaptation and learning, they have the negative attribute of a "black box" syndrome. By the same token, FL has the advantage of approximate reasoning but the disadvantage that it lacks an effective learning capability. Merging these technologies provides an opportunity to capitalize on their strengths and compensate for their shortcomings. This article presents an overview of the merging of NNs, FL and GAs. The topics addressed include the advantages and disadvantages of each technology, the potential merging options, and the explicit nature of the merging.