CGA

Cluster Genetic Algorithm (CGA)

Usage

CGA is performed by executing run_GA.py.

Simulation parameters

• rseed, random seed
• T, temperature
• nseeds, the number of Pd atoms

Genetic algorithm (GA) hyperparameters

• npop, size of population
• ngen, number of generations
• nfitness, the number of fitness attribute
• cxpb, the probability of mating two individuals
• nremain, the number of individual selected in each iteration

Flow chart and operators

Output files describing a CGA trajectory

• ga_history_output_n_Tk.csv, the record for each individual in each generation
• ga_generation_best_n_Tk.csv, the record for the fittest individual in each generation
• ga_stats_output_n_Tk.csv, the record for the statstics of all individuals in each generation
• ga_hall_of_frame_n_Tk.csv, the record for the top physically possible individuals in all generations

Post-processing

• Use process_GA_single.py to read all output files and generate GA trajectory plots
• Use process_physics_single.py to read all output files and generate CNs for each individual
• Use plot_physics_single.py to generate CN trajectories