GAPORE: boolean network inference using a genetic algorithm with novel polynomial representation and encoding scheme

Research output: Contribution to journalArticlepeer-review


  • Xiang Liu
  • Yan Wang
  • Ning Shi
  • Zhicheng Ji
  • Shan He

Colleges, School and Institutes


Inferring Boolean networks is crucial for modeling and analyzing gene regulatory networks from a systematic perspective. However, the state-of-the-art algorithms cannot accurately infer the topology and dynamics of Boolean networks due to the lack of an efficient approach to representing the unknown Boolean functions and the over-fit problem caused by the noise in time-series data. To address these problems, we propose a novel inference algorithm using a genetic algorithm with novel polynomial representation and encoding scheme (GAPORE) to reconstruct large-scale Boolean networks accurately. First of all, a novel symbolic polynomial representation method is introduced to efficiently represent the unknown Boolean functions of the candidate Boolean network as the symbolic polynomial dynamical equations. Then, a novel encoding scheme is developed to flexibly encode the symbolic polynomial dynamical equations by varying the effective lengths of the chromosomes. To reduce the over-fit problem, the -norm regularization is designed into the fitness evaluation in view of the network sparsity. In addition, the local search strategy is embedded into the hybrid genetic algorithm framework to strengthen the search capability. Extensive experiments demonstrate that GAPORE can infer the large-scale Boolean networks more accurately than state-of-the-art algorithms from the noisy time-series data.


Original languageEnglish
Article number107277
JournalKnowledge-Based Systems
Early online date3 Jul 2021
Publication statusE-pub ahead of print - 3 Jul 2021


  • Data-driven, Boolean network, Polynomial Boolean representation, Hybrid genetic algorithm, Dominant bit encoding scheme