Accelerating Vegetation Evolution with Mutation Strategy and Gbased Growth Strategy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose two strategies, mutation strategy and Gbased growth strategy, to enhance the performance of standard vegetation evolution (VEGE) that simulates the growth and reproduction of vegetation repeatedly to find the global optimum. We introduce two different mutation methods into the growth period and the maturity period individually to increase the diversity of population by simulating different types of mutations in real plants. Inspired by various growth patterns of real plants, the Gbased growth strategy is proposed to replace a completely random growth operation of standard VEGE and bias all non-optimal individuals to grow towards the current best area. We design a series of controlled experiments to evaluate the performance of our proposed strategies using 28 benchmark functions from CEC2013 suite with three different dimensions. The experimental results confirmed the mutation strategy can increase the diversity and the Gbased growth strategy plays an important role in accelerating convergence. Besides, the combination of both strategies can further improve the VEGE performance.

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3033-3039
Number of pages7
ISBN (Electronic)9781728124858
DOIs
Publication statusPublished - Dec 2019
Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
Duration: Dec 6 2019Dec 9 2019

Publication series

Name2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

Conference

Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
CountryChina
CityXiamen
Period12/6/1912/9/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Modelling and Simulation

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  • Cite this

    Yu, J., & Takagi, H. (2019). Accelerating Vegetation Evolution with Mutation Strategy and Gbased Growth Strategy. In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 3033-3039). [9003027] (2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI44817.2019.9003027