Evolving optimal fuzzy-connective-based hierarchical aggregation networks using genetic algorithms

Multilayer fuzzy connective-based hierarchical aggregation networks provide a flexible and intuitive approach to decision analysis. This approach simulates the decision making processes performed by humans, and the results can be interpreted as a set of rules with which to fashion an abstract model of the problem. Identifying the relative importance of the criteria helps to identify redundancies that do not contribute to the decision-making process. However, a gradient-based learning approach tends to generate local solutions, and requires the aggregation function to be continuous and differentiable. This study proposes a GA-based learning approach to identify the connective parameters, exploiting the global exploration ability of GAs to improve the quality of solutions. This approach does not require gradient information, making it applicable to both differentiable and nondifferentiable aggregation functions. The benefits of this method were demonstrated using eight datasets with different characteristics. Statistical analysis of the experimental results confirms that the proposed approach outperforms the gradient-based learning approach, generating more accurate estimates for both generalized mean and gamma operators. The proposed approach is well suited to a broad range of fuzzy aggregation connectives, which further expands its applicability.