A divide and conquer method for learning large Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling and simulating dynamic systems. FCMs were applied in a large number of diverse areas and have already gained momentum due to their simplicity and easiness of use. However, these models are usually generated manually, and thus they cannot be applied when dealing with large number of variables. In such cases, their development could be significantly affected by the limited knowledge and skills of the designer. In the past few years we have witnessed the development of several methods that support experts in establishing the FCMs or even replace humans by automating the construction of the maps from data. One of the problems of the existing automated methods is their limited scalability, which results in inability to handle large number of variables. The proposed method applies a divide and conquer strategy to speed up a recently proposed genetic optimization of FCMs. We empirically contrast several different designs, including parallelized genetic algorithms, FCM-specific designs based on sampling of the input data, and existing Hebbian-based methods. The proposed method, which utilizes genetic algorithm to learn and merge multiple FCM models that are computed from subsets of the original data, is shown to be faster than other genetic algorithm-based designs while resulting in the FCMs of comparable quality. We also show that the proposed method generates FCMs of higher quality than those obtained with the use of Hebbian-based methods.