def make_new_evolution(name, confusion, mask):
print('Making new evolution')
evolution = Evolution(confusion, mask)
evolution.run_tests()
ranked_preys = evolution.rank_preys()
ranked_predators = evolution.rank_predators()
evolution.update_pools(ranked_predators, ranked_preys)
evolution.save(name)
return evolution
if showViz:
FitnessGraph = Viz.createGraph()
Viz.labelGraph(FitnessGraph, "FitnessGraph")
Viz.legendGraph(FitnessGraph, "Max Fitness", c="red")
Viz.setStruts(num_bars)
#smaller stru ts, less play but faster convergence.....
GA = Evolution(num_struts = num_bars, strut_length = 10, max_gen=1000,init_size =population, pop_size=population, mutation_rate=m_rate, \
selection_rate = s_rate, selection_pressure = s_pressure,elite_rate=e_rate) #Essentailly just have an autmated hill climber rn, b/c mutation rate is so highself.
GA.load(load)
GA.eps = 1
while GA.alive():
if GA.current_gen % 25 == 0:
GA.save(population)
print("----------------------")
print("Curr Gen: ", GA.current_gen)
print("Pop Sicze", len(GA.pop))
for i in np.arange(len(GA.pop)):
drone_structure = GA.pop[i][2] # loop through current population
Solver.solve(drone_structure,
i) # drones have already been solved so they go very fast
fit = GA.fitness(drone_structure) # evaluate drone
drone_structure.fitness = fit # update fitness
GA.addToQueue(drone_structure, fit) # add drone the the queue
GA.niche(niche_radius)
GA.rankQueue()
if showViz:
