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

Abstract

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 ordered weighted average (WOWA). We give the formal definitions for each of the aggregation operators, explain the two learning methods, give results of processing for each of the methods and operators with simple test datasets, and contrast the approaches and results.