In this approach, the system is modeled as a grand canonical ensemble consisting of chosen. The initial candidate pool is generated using the bond length distribution algorithm (BLDA), randomly generating structures based on covalent radii . A preoptimization step using Hookean potentials ensures reasonable initial geometries before local optimization via MLIP and energy evaluation. Offspring generation involves a split-and-splice method , where parent slabs are cut along random planes and then recombined. A fitness score based on the mating frequency and grand canonical free energy is assigned to each candidate, favoring those with lower free energies and less frequent mating for further mating. A similarity check ensures no duplicates are added to the population. Mutation operations include adding or removing adsorbates, randomly displacing surface atoms, translating the buffer slab along the x-or y-axis by 1/n (n = 2, 3, 6) of the cell length, and permuting half of the buffer region. If offspring is too similar to their parents, their mutation rate is increased to 100%. The lowest fitness candidate is archived after each new addition to maintain population size. To conserve computational resources and avoid divergence due to MLIP instability, a maximum of 300 relaxation steps are imposed, similar to the VSSR-MC method.