ml4co_kit.solver.routing.ga_eax

GA-EAX (A Genetic Algorithm using Edge Assembly Crossover)

Classes

GAEAXSolver([ga_eax_scale, ...])

GA-EAX: https://github.com/nagata-yuichi/GA-EAX Current Version: 015dfbe9f267230f78787bd244af393ffc018900 Last Update: 2026-05-26 @article{ nagata2013powerful, title={A powerful genetic algorithm using edge assembly crossover for the traveling salesman problem}, author={Nagata, Yuichi and Kobayashi, Shigenobu}, journal={INFORMS Journal on Computing}, volume={25}, number={2}, pages={346--363}, year={2013}, publisher={INFORMS} }

class ml4co_kit.solver.routing.ga_eax.GAEAXSolver(ga_eax_scale: int = 100000.0, ga_eax_max_trials: int = 1, ga_eax_population_num: int = 100, ga_eax_offspring_num: int = 30, ga_eax_show_info: bool = False, use_large_solver: bool = False, optimizer: OptimizerBase = None)[source]

Bases: SolverBase

GA-EAX: https://github.com/nagata-yuichi/GA-EAX Current Version: 015dfbe9f267230f78787bd244af393ffc018900 Last Update: 2026-05-26 @article{

nagata2013powerful, title={A powerful genetic algorithm using edge assembly crossover for the traveling salesman problem}, author={Nagata, Yuichi and Kobayashi, Shigenobu}, journal={INFORMS Journal on Computing}, volume={25}, number={2}, pages={346–363}, year={2013}, publisher={INFORMS}

}