#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:
if GA.current_gen % updatefreq == 0:
Viz.plotGraph(FitnessGraph,
GA.getMaxFitness(),
GA.current_gen,
c='red')
Viz.plotGraph(FitnessGraph,
GA.getAvgFitness(),
GA.current_gen,
