Evolutionary multitasking fuzzy cognitive map learning

Abstract

In real-world applications, there exist multiple fuzzy cognitive maps (FCMs) learning tasks with similar attributes that have to be optimized simultaneously, however, all existing algorithms were designed to learn single FCM without considering the valuable patterns that can share with each other. For the purpose of making use of similar structure patterns among different tasks, we introduce the evolutionary multitasking framework to learn different FCMs at one time by taking each FCM learning problem as a task. Most proposed evolutionary-based algorithms learn FCMs from time series by minimizing data error which evaluates the difference between generated response sequences and available response sequences, which did not take the sparsity of the weight matrix into consideration. To learn large-scale FCMs for each task, in this paper we adopt a decomposition strategy based multiobjective optimization algorithm considering both the measure error and sparsity of FCMs. Moreover, the memetic algorithm and LASSO initialization operator are incorporated into the multitasking framework to improve the performance and accelerate the convergence. Through the whole process, we find that multitasking optimization can not only learn various FCMs in a population but also improve the accuracy of similar tasks by taking the advantage of gene transfer for similar patterns. Extensive experiments on two-task FCM learning problems with varying number of nodes, densities and activation functions and the application for the problem of reconstructing gene regulatory networks have been conducted to illustrate that the proposal can learn large-scale FCMs with low errors in a fast convergence speed.