Adaptive genetic network programming

Xianneng Li, Wen He, Kotaro Hirasawa

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Genetic Network Programming (GNP) is derived from Genetic Algorithm (GA) and Genetic Programming (GP), which applies evolution theory to evolve a population of directed graph to model complex systems. It has been shown that GNP can solve typical control problems, as well as many real-world problems. However, studying GNP is mainly focused on the specific aspect, while the fundamental characteristics that ensure the success of GNP are rarely investigated in the previous research. This paper reveals an important feature of GNP - reusability of nodes - to efficiently identify and formulate the building blocks of evolution. Accordingly, adaptive GNP is developed which self-adapts both crossover and mutation probabilities of each search variable to circumstances. The adaptation allows the automatic adjustment of evolution bias toward the frequently reused nodes in high-quality individuals. The adaptive GNP is compared with traditional GNP in a benchmark control testbed to evaluate its superiority.

本文言語English
ホスト出版物のタイトルProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1808-1815
ページ数8
ISBN(印刷版)9781479914883
DOI
出版ステータスPublished - 2014 9 16
イベント2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing
継続期間: 2014 7 62014 7 11

Other

Other2014 IEEE Congress on Evolutionary Computation, CEC 2014
CityBeijing
Period14/7/614/7/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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