new usage

README.rst

@@ -70,35 +70,29 @@ However, for instance executing NSGA2:
 
 .. code:: python
 
+   from pymoo.optimize import minimize
+   from pymoo.algorithms.nsga2 import nsga2
+   from pymoo.util import plotting
+   from pymop.factory import get_problem
 
-    import numpy as np
-
-    from pymoo.optimize import minimize
-    from pymoo.util import plotting
-    from pymop.factory import get_problem
-
-    # create the optimization problem
-    problem = get_problem("zdt1")
-    pf = problem.pareto_front()
-
-    res = minimize(problem,
-                   method='nsga2',
-                   method_args={'pop_size': 100},
-                   termination=('n_gen', 200),
-                   pf=pf,
-                   save_history=True,
-                   disp=True)
-
-    plot = True
-    if plot:
-        plotting.plot(pf, res.F, labels=["Pareto-front", "F"])
-
-    # set true if you want to save a video
-    animate = False
-    if animate:
-        from pymoo.util.plotting import animate as func_animtate
-        H = np.concatenate([e.pop.get("F")[None, :] for e in res.history], axis=0)
-        func_animtate('%s.mp4' % problem.name(), H, problem)
+   # load a test or define your own problem
+   problem = get_problem("zdt1")
+
+   # get the optimal solution of the problem for the purpose of comparison
+   pf = problem.pareto_front()
+
+   # create the algorithm object
+   method = nsga2(pop_size=100, elimate_duplicates=True)
+
+   # execute the optimization
+   res = minimize(problem,
+                  method,
+                  termination=('n_gen', 200),
+                  pf=pf,
+                  disp=True)
+
+   # plot the results as a scatter plot
+   plotting.plot(pf, res.F, labels=["Pareto-Front", "F"])
 
 
 

setup.py

@@ -23,7 +23,7 @@ def get_extension_modules():
     url=__url__,
     python_requires='>3.3',
     author_email="blankjul@egr.msu.edu",
-    description="Multi-Objective Optimization Algorithms",
+    description="Multi-Objective Optimization in Python",
     long_description=readme(),
     license='Apache License 2.0',
     keywords="optimization",
@@ -32,16 +32,18 @@ def get_extension_modules():
     include_package_data=True,
     platforms='any',
     classifiers=[
-        'Development Status :: Production/Stable',
+        'Intended Audience :: Developers',
         'Intended Audience :: Science/Research',
-        'License :: OSI Approved :: Apache License',
         'Operating System :: OS Independent',
+        'License :: OSI Approved :: Apache Software License',
         'Programming Language :: Python',
         'Programming Language :: Python :: 3',
         'Programming Language :: Python :: 3.4',
         'Programming Language :: Python :: 3.5',
         'Programming Language :: Python :: 3.6',
-        'Topic :: Optimization :: Multi-objective Optimization',
+        'Topic :: Scientific/Engineering',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        'Topic :: Scientific/Engineering :: Mathematics'
     ]
 )