<jupyter_start><jupyter_text>ZDT<jupyter_code># start the problem
%matplotlib notebook
problem_name = "ZDT1"
problem = test_problem_builder(problem_name)

# run ibea
# step 0. Let's start with rough approx
ib = IBEA(problem, population_size=32, n_iterations=10, n_gen_per_iter=100,total_function_evaluations=10000)
while ib.continue_evolution():
    ib.iterate()
individuals, objective_values = ib.end()
#print(objective_values)
# show some of the ibea pop somehow
print("IBEA ideal",ib.population.problem.ideal)
plt.scatter(x=objective_values[:,0], y=objective_values[:,1], label="IBEA Front")
plt.title(f"IBEA approximation")
plt.xlabel("F1")
plt.ylabel("F2")
plt.legend()
plt.show()<jupyter_output>IBEA ideal [0.        0.0009566]<jupyter_text>DTLZ<jupyter_code>#problem_name = "DTLZ1" 
#problem = test_problem_builder(problem_name, n_of_variables=7, n_of_objectives=3)
    
#problem_name = "DTLZ2" 
#problem = test_problem_builder(problem_name, n_of_variables=12, n_of_objectives=3)

problem_name = "DTLZ4"
problem = test_problem_builder(problem_name, n_of_variables=12, n_of_objectives=3)

#problem_name = "DTLZ6"
#problem = test_problem_builder(problem_name, n_of_variables=12, n_of_objectives=3)

#problem_name = "DTLZ7"
#problem = test_problem_builder(problem_name, n_of_variables=22, n_of_objectives=3)
# run ibea
# step 0. Let's start with rough approx
ib = IBEA(problem, population_size=100, n_iterations=10, n_gen_per_iter=100,total_function_evaluations=5000)
while ib.continue_evolution():
    ib.iterate()
individuals, objective_values = ib.end()
print(ib.population.problem.ideal)
# show some of the ibea pop somehow
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.view_init(30,45)
ax.scatter(objective_values[:,0],objective_values[:,1],objective_values[:,2], label="IBEA Front")
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()<jupyter_output>[3.75388256e-33 0.00000000e+00 0.00000000e+00]
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