Empirical evaluation of changing crossover operators to solve function optimization problems

@article{Takahashi2016EmpiricalEO,
  title={Empirical evaluation of changing crossover operators to solve function optimization problems},
  author={R. Takahashi},
  journal={2016 IEEE Symposium Series on Computational Intelligence (SSCI)},
  year={2016},
  pages={1-10}
}
  • R. Takahashi
  • Published 2016
  • Computer Science
  • 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
In this paper, the effectiveness of methodologies for changing crossover operators (CXOs) to solve function optimization problems (FOP) are empirically validated in order to solve the problems of premature convergence in genetic algorithms. CXOs are methods of finding solutions for combinatorial optimization problems through genetic algorithms (GAs) while maintaining the balance of satisfying the contrary requisites for GAs: to sustain the diversity of the population and to improve the… Expand
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