A comparison of active set method and genetic algorithm approaches for learning weighting vectors in some aggregation operators

In this article we compare two contrasting methods, active set method (ASM) and genetic algorithms, for learning the weights in aggregation operators, such as weighted mean (WM), ordered weighted average (OWA), and weighted o rdered weighted average (WOWA). We give the formal definitions for each of the aggregation operators, explain the two learning methods, give results o f processing for each of the methods and operators with simple test dataset s, and contrast the approaches and results. (C) 2001 John Wiley & Sons, Inc .