Evolving choice structures for genetic programming

Shuaiqiang Wang, Jun Ma*, Jiming LIU, Xiaofei Niu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


It is quite difficult but essential for Genetic Programming (GP) to evolve the choice structures. Traditional approaches usually ignore this issue. They define some "if-structures" functions according to their problems by combining "if-else" statement, conditional criterions and elemental functions together. Obviously, these if-structure functions depend on the specific problems and thus have much low reusability. Based on this limitation of GP, in this paper we propose a kind of termination criterion in the GP process named "Combination Termination Criterion" (CTC). By testing CTC, the choice structures composed of some basic functions independent to the problems can be evolved successfully. Theoretical analysis and experiment results show that our method can evolve the programs with choice structures effectively within an acceptable additional time.

Original languageEnglish
Pages (from-to)871-876
Number of pages6
JournalInformation Processing Letters
Issue number20
Publication statusPublished - 30 Sept 2010

Scopus Subject Areas

  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Computer Science Applications

User-Defined Keywords

  • Choice structure
  • Evolutionary computation
  • Genetic programming
  • Program derivation


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