Multi-species Generation Strategy-Based Vegetation Evolution

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

We propose a multi-species generation strategy to increase the diversity of seed individuals produced in the maturity operation of vegetation evolution (VEGE). Since the breeding patterns of real plants can be roughly divided into sexual reproduction and asexual one, the proposed strategy additionally introduces two different methods to simulate these two patterns. As our preliminary attempt of the simulation, a mature individual is splattered randomly in the neighbor local area of its parent individual with Gaussian distribution probability to simulate asexual reproduction, while a mature individual is generated by crossing randomly selected two different parent individuals to simulate sexual reproduction. Our proposed strategy consists of these two new reproduction methods and that of our original VEGE, and each mature individual in every generation randomly selects one of these three methods to generate seed individuals, which is analogous to different plant species using different mechanisms to breed. To evaluate the performance of our proposed strategy, we compare VEGE and (VEGE + the proposed generation strategy) on 28 benchmark functions of three different dimensions from the CEC 2013 test suit with 30 independent trial runs. The experimental results have confirmed that the proposed strategy can increase the diversity of seed individuals, accelerate the convergence of VEGE significantly, and become effective according to the increase of dimensions.

本文言語英語
ホスト出版物のタイトル2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728169293
DOI
出版ステータス出版済み - 7 2020
イベント2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, 英国
継続期間: 7 19 20207 24 2020

出版物シリーズ

名前2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

会議

会議2020 IEEE Congress on Evolutionary Computation, CEC 2020
国/地域英国
CityVirtual, Glasgow
Period7/19/207/24/20

All Science Journal Classification (ASJC) codes

  • 制御と最適化
  • 決定科学(その他)
  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • ハードウェアとアーキテクチャ

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