Evolving graph-based chromosome by means of variable size genetic network programming with binomial distribution

Bing Li, Xianneng Li, Shingo Mabu, Kotaro Hirasawa

Research output: Contribution to journalArticle

1 Citation (Scopus)


Genetic network programming (GNP) is a graph-based evolutionary algorithm with fixed size, which has been proven to solve complicated problems efficiently and effectively. In this paper, variable size genetic network programming (GNPvs) with binomial distribution has been proposed, which will change the size of the individuals and obtain their optimal size during evolution. The proposed method will select the number of nodes to move from one parent GNP to another parent GNP during crossover to implement the new feature of GNP. The probability of selecting the number of nodes to move satisfies a binomial distribution. The proposed method can keep the effectiveness of crossover, improve the performance of GNP, and find the optimal size of the individuals. The well-known testbed Tileworld is used to show the numerical results in the simulations.

Original languageEnglish
Pages (from-to)348-356
Number of pages9
JournalIEEJ Transactions on Electrical and Electronic Engineering
Issue number4
Publication statusPublished - 2013 Jul



  • Binomial distribution
  • Crossover
  • Genetic network programming
  • Tileworld
  • Variable size

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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