A new hybrid real-coded genetic algorithm and its application to parameters identification of soils

Yin Fu Jin, Zhen Yu Yin, Shui Long Shen, Dong Mei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

49 Citations (Scopus)


Inverse analysis using an optimization method based on a genetic algorithm (GA) is a useful tool for obtaining soil parameters in geotechnical fields. However, the performance of the optimization in identifying soil parameters mainly depends on the search ability of the GA used. This study aims to develop a new efficient hybrid real-coded genetic algorithm (RCGA) being applied to identify parameters of soils. In this new RCGA, a new hybrid strategy is proposed by adopting two crossovers with outstanding ability, namely the Simulated Binary Crossover and the simplex crossover. In order to increase the convergence speed, a chaotic local search technique is used conditionally. The performance of the proposed RCGA is first validated by optimizing mathematical benchmark functions. The results demonstrate that the RCGA has an outstanding search ability and faster convergence speed compared to other hybrid RCGAs. The proposed new hybrid RCGA is then further evaluated by identifying soil parameters based on both laboratory tests and field tests, for different soil models. All the comparisons demonstrate that the proposed RCGA has an excellent performance of inverse analysis in identifying soil parameters, and is thus recommended for use based on all the evaluations carried out in this paper.

Original languageEnglish
Pages (from-to)1343-1366
Number of pages24
JournalInverse Problems in Science and Engineering
Issue number9
Publication statusPublished - 2 Sep 2017
Externally publishedYes


  • laboratory and field tests
  • optimization
  • parameter identification
  • RCGA
  • soil models

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Applied Mathematics

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