Neural Processing Letters

, Volume 50, Issue 3, pp 2843–2870 | Cite as

A Stable, Unified Density Controlled Memetic Algorithm for Gene Regulatory Network Reconstruction Based on Sparse Fuzzy Cognitive Maps

  • Yilan Wang
  • Jing LiuEmail author


Gene regulatory networks (GRNs) denote the interrelation among genes in the genomic level. GRNs have a sparse network structures, and as a simulation of GRNs, the density of The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge is less than 5%. So using sparse models to represent GRNs is a meaningful task. Fuzzy cognitive maps (FCMs) have been used to reconstruct GRNs. However, the networks learned by automated derivate-free methods is much denser than those in practical applications. Moreover, the performance of current sparse FCM learning algorithms is worse than what we expect. Therefore, proposing a fast, simple and sparse FCM learning algorithm is a realistic demand. Here, we propose a new unified algorithm: Density Controlled Memetic Algorithm (DC-MA) for learning sparse FCMs. As a simple and good-performance algorithm, memetic algorithm (MA) is chosen as the framework of DC-MA. In DC-MA, a new crossover operator and a mutation operator are designed to optimize the target, control the density and ensure the stability; the local search is used to improve the accuracy and a special self-learning operator is proposed to adjust density. To test the effectiveness of our algorithm, DC-MA is performed on synthetic data with varying sizes and densities. The results show that DC-MA obtains good performance in learning sparse FCMs from time series. On the benchmark datasets DREAM3, DREAM4 and large-scale GRN reconstruction DREAM5 dataset, DC-MA shows high accuracy. The good performance in learning sparse FCMs shows the effectiveness of DC-MA, and the simplicity and scalability of the framework ensure that DC-MA can be adapted to a wide range of needs.


Fuzzy cognitive maps Density controlled operators Self-learning strategy Memetic algorithm 



This work was supported in part by the General Program of National Natural Science Foundation of China (NSFC) under Grant 61773300 and in part by the Key Program of Fundamental Research Project of Natural Science of Shaanxi Province, China under Grant 2017JZ017.

Supplementary material

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Supplementary material 1 (DOCX 45 kb)


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Intelligent Perception and Image Understanding of Ministry of EducationXidian UniversityXi’anChina

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