Learning and evolution of genetic network programming with knowledge transfer

Xianneng Li*, Wen He, Kotaro Hirasawa

*この研究の対応する著者

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Traditional evolutionary algorithms (EAs) generally starts evolution from scratch, in other words, randomly. However, this is computationally consuming, and can easily cause the instability of evolution. In order to solve the above problems, this paper describes a new method to improve the evolution efficiency of a recently proposed graph-based EA - genetic network programming (GNP) - by introducing knowledge transfer ability. The basic concept of the proposed method, named GNP-KT, arises from two steps: First, it formulates the knowledge by discovering abstract decision-making rules from source domains in a learning classifier system (LCS) aspect; Second, the knowledge is adaptively reused as advice when applying GNP to a target domain. A reinforcement learning (RL)-based method is proposed to automatically transfer knowledge from source domain to target domain, which eventually allows GNP-KT to result in better initial performance and final fitness values. The experimental results in a real mobile robot control problem confirm the superiority of GNP-KT over traditional methods.

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798-805
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

  • 人工知能
  • 計算理論と計算数学
  • 理論的コンピュータサイエンス

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