Comparison between genetic network programming and genetic programming using evolution of ant's behaviors

Kotaro Hirasawa, Masafumi Okubo, Takayuki Furuzuki

Research output: Contribution to journalArticle

Abstract

Recently, many methods of evolutionary computation such as Genetic Algorithm(GA) and Genetic Programming(GP) have been developed as a basic tool for modeling and optimizing the complex systems. Generally speaking, GA has the genome of string structure, while the genome in GP is the tree structure. Therefore, GP is suitable to construct the complicated programs, which can be applied to many real world problems. But, GP is sometimes difficult to search for a solution because of its bloat and introns. In this paper, a new evolutionary method named Genetic Network Programming(GNP), whose genome is network structure is proposed to overcome the low searching efficiency of GP and is applied to the problem on the evolution of behaviors of ants in order to study the effectiveness of GNP. In addition, the comparison of the performances between GNP and GP is carried out in simulations on ants behaviors.

Original languageEnglish
Pages (from-to)31-37
Number of pages7
JournalResearch Reports on Information Science and Electrical Engineering of Kyushu University
Volume6
Issue number1
Publication statusPublished - 2001 Mar
Externally publishedYes

Fingerprint

Genetic programming
Computer programming
Genes
Genetic algorithms
Evolutionary algorithms
Large scale systems

Keywords

  • Artificial life
  • Evolutionary computation
  • Genetic algorithm
  • Genetic programming

ASJC Scopus subject areas

  • Hardware and Architecture
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

Cite this

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