Optimization Performance Analysis for Adaptive Genetic Algorithm with Nonlinear Probabilities

Genetic Algorithm (GA) has been proven to be easy in falling into local optimal value due to its fixed crossover probability and mutation probability, while Adaptive Genetic Algorithm (AGA) has strong global search capability because the two probabilities adjust adaptively. There are two categories of AGA according to the different adjustment methods for crossover and mutation probabilities: probabilistic linear adjustment AGA and probabilistic non-linear adjustment AGA. AGA with linear adjustment of probability values cannot solve the problems of local optimal value and premature convergence. The nonlinear adaptive probability adjustment strategy can avoid premature convergence, poor stability and slow convergence speed. The typical AGA with nonlinear adjustment of probabilities are compared and analyzed through benchmark functions. The optimization performance of typical AGA algorithms is compared and analyzed by 10 benchmark functions. Compared with traditional GA and other AGA algorithms, AGA with crossover and mutation probabilities adjusted nonlinearly at both ends of the average fitness value has higher computational stability and is easy to find the global optimal solution, which provides ideas for the application of adaptive genetic algorithm.

@article{Sun2022,
  title     = {Optimization Performance Analysis for Adaptive Genetic Algorithm with Nonlinear Probabilities},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {6},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Wenjuan Sun and Qiaoping Su and Hongli Yuan and Yan Chen},
  doi       = {10.14569/IJACSA.2022.0130645},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130645}
}