GENETIC ALGORITHMS FOR DETERMINING FUZZY MEASURES FROM DATA

A synthetic evaluation of a given object in terms of multiple factors that contribute to some feature of the object (quality, performance, e tc.) may be regarded as a system with multiple inputs and one output. Traditionally, the output is expressed as the weighted average of the inputs. Unfortunately, this method is severely limited as it cannot ca pture any inherent relation among the factors involved. This limitatio n can be overcome by using the Choquet integral or the fuzzy integral with respect to a fuzzy measure that captures the relation among the f actors. The crux of this method is to determine the right fuzzy measur e. In this paper, we describe an efficient genetic algorithm for const ructing a suitable fuzzy measure from relevant input-output data. This algorithm has a broad applicability in various problem areas, such as decision making, cluster analysis, pattern recognition, image and spe ech processing, and expert systems.