Comparison of Genetic and Gradient Descent Algorithms for Determining Fuzzy Measures

The increasing usage of fuzzy integrals as aggregation operators, such as Sugeno and Choquet integrals, is threatened by the crux of determining the fuzzy measures in real problems. One way to determine these measures is by using historical data and tuning the parameters. During the past decade many algorithms have been introduced for this purpose and in this paper we make a comparison between two well known, genetic algorithm and gradient descent. Our objective is to assess which algorithm performs better for usage in real applications; hence we use two illustrative cases to compare the algorithms. The results show that gradient descent performs better in recognizing the pattern in less time and with less deviation