Continuous probabilistic model building genetic network programming using reinforcement learning

Xianneng Li, Kotaro Hirasawa*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Recently, a novel probabilistic model-building evolutionary algorithm (so called estimation of distribution algorithm, or EDA), named probabilistic model building genetic network programming (PMBGNP), has been proposed. PMBGNP uses graph structures for its individual representation, which shows higher expression ability than the classical EDAs. Hence, it extends EDAs to solve a range of problems, such as data mining and agent control. This paper is dedicated to propose a continuous version of PMBGNP for continuous optimization in agent control problems. Different from the other continuous EDAs, the proposed algorithm evolves the continuous variables by reinforcement learning (RL). We compare the performance with several state-of-the-art algorithms on a real mobile robot control problem. The results show that the proposed algorithm outperforms the others with statistically significant differences.

Original languageEnglish
Pages (from-to)457-467
Number of pages11
JournalApplied Soft Computing Journal
Publication statusPublished - 2015


  • Continuous optimization
  • Estimation of distribution algorithm
  • Network programming
  • Probabilistic model building genetic
  • Reinforcement learning

ASJC Scopus subject areas

  • Software


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