Remove convenience functions from all files and fix NSGA2 performance

.gitignore

@@ -6,6 +6,7 @@
 **/*.gif
 **/*.cpp
 **/nohup.out
+benchmark
 
 
 **/dask-worker-space/**

docs/source/algorithms/algorithms.csv

@@ -1,19 +1,19 @@
-    :ref:`Genetic Algorithm <nb_ga>`, GA, ga, single, x,A modular implementation of a genetic algorithm. It can be easily customized with different evolutionary operators and applies to a broad category of problems.
-   :ref:`Differential Evolution <nb_de>`, DE, de, single, x, Different variants of differential evolution which is a well-known concept for in continuous optimization especially for global optimization.
-   :ref:`Biased Random Key Genetic Algorithm <nb_brkga>`, BRKGA, brkga, single, x, Mostly used for combinatorial optimization where instead of custom evolutionary operators the complexity is put into an advanced variable encoding.
-   :ref:`Nelder Mead <nb_nelder_mead>`, NelderMead, nelder-mead, single, x, A point-by-point based algorithm which keeps track of a simplex with is either extended  reflected or shrunk.
-   :ref:`Pattern Search <nb_pattern_search>`, PatternSearch, pattern-search, single, x, Iterative approach where the search direction is estimated by forming a specific exploration pattern around the current best solution.
-   :ref:`CMAES <nb_cmaes>`, CMAES, cmaes, single, , Well-known model-based algorithm sampling from a dynamically updated normal distribution in each iteration.
-   :ref:`Evolutionary Strategy <nb_es>`, ES, es, single, , The evolutionary strategy algorithm proposed for real-valued optimization problems.
-   :ref:`Stochastic Ranking Evolutionary Strategy <nb_sres>`, SRES, sres, single, x, An evolutionary strategy with constrained handling using stochastic ranking.
-   :ref:`Improved Stochastic Ranking Evolutionary Strategy <nb_isres>`, ISRES, isres, single, x, An improved version of SRES being able to deal dependent variables efficiently.
-   :ref:`NSGA-II <nb_nsga2>`, NSGA2, nsga2, multi, x, Well-known multi-objective optimization algorithm based on non-dominated sorting and crowding.
-   :ref:`R-NSGA-II <nb_rnsga2>`, RNSGA2, rnsga2, multi, x, An extension of NSGA-II where reference/aspiration points can be provided by the user.
-   :ref:`NSGA-III <nb_nsga3>`, NSGA3, nsga3, many, x, An improvement of NSGA-II developed for multi-objective optimization problems with more than two objectives.
-   :ref:`U-NSGA-III <nb_unsga3>`, UNSGA3, unsga3, many, x, A generalization of NSGA-III to be more efficient for single and bi-objective optimization problems.
-   :ref:`R-NSGA-III <nb_rnsga3>`, RNSGA3, rnsga3, many, x, Allows defining aspiration points for NSGA-III to incorporate the user's preference.
-   :ref:`MOEA\D <nb_moead>`, MOEAD, moead, many,, Another well-known multi-objective optimization algorithm based on decomposition.
-   :ref:`AGE-MOEA <nb_agemoea>`, AGEMOEA, agemoea, many,, Similar to NSGA-II but estimates the shape of the Pareto-front to compute a score replacing the crowding distance.
-   :ref:`C-TAEA <nb_ctaea>`, CTAEA, ctaea, many, x, An algorithm with a more sophisticated constraint-handling for many-objective optimization algoritms.
-      :ref:`SMS-EMOA <nb_sms>`, CTAEA, ctaea, many, x, An algorithm that uses hypervolume during the environmental survival.
-      :ref:`RVEA <nb_rvea>`, RVEA, ctaea, many, x, A reference direction based algorithm used an angle-penalized metric.
+    :ref:`Genetic Algorithm <nb_ga>`, GA, single, x,A modular implementation of a genetic algorithm. It can be easily customized with different evolutionary operators and applies to a broad category of problems.
+   :ref:`Differential Evolution <nb_de>`, DE, single, x, Different variants of differential evolution which is a well-known concept for in continuous optimization especially for global optimization.
+   :ref:`Biased Random Key Genetic Algorithm <nb_brkga>`, BRKGA, single, x, Mostly used for combinatorial optimization where instead of custom evolutionary operators the complexity is put into an advanced variable encoding.
+   :ref:`Nelder Mead <nb_nelder_mead>`, NelderMead, single, x, A point-by-point based algorithm which keeps track of a simplex with is either extended  reflected or shrunk.
+   :ref:`Pattern Search <nb_pattern_search>`, PatternSearch, single, x, Iterative approach where the search direction is estimated by forming a specific exploration pattern around the current best solution.
+   :ref:`CMAES <nb_cmaes>`, CMAES, single, , Well-known model-based algorithm sampling from a dynamically updated normal distribution in each iteration.
+   :ref:`Evolutionary Strategy <nb_es>`, ES, single, , The evolutionary strategy algorithm proposed for real-valued optimization problems.
+   :ref:`Stochastic Ranking Evolutionary Strategy <nb_sres>`, SRES, single, x, An evolutionary strategy with constrained handling using stochastic ranking.
+   :ref:`Improved Stochastic Ranking Evolutionary Strategy <nb_isres>`, ISRES, single, x, An improved version of SRES being able to deal dependent variables efficiently.
+   :ref:`NSGA-II <nb_nsga2>`, NSGA2, multi, x, Well-known multi-objective optimization algorithm based on non-dominated sorting and crowding.
+   :ref:`R-NSGA-II <nb_rnsga2>`, RNSGA2, multi, x, An extension of NSGA-II where reference/aspiration points can be provided by the user.
+   :ref:`NSGA-III <nb_nsga3>`, NSGA3, many, x, An improvement of NSGA-II developed for multi-objective optimization problems with more than two objectives.
+   :ref:`U-NSGA-III <nb_unsga3>`, UNSGA3, many, x, A generalization of NSGA-III to be more efficient for single and bi-objective optimization problems.
+   :ref:`R-NSGA-III <nb_rnsga3>`, RNSGA3, many, x, Allows defining aspiration points for NSGA-III to incorporate the user's preference.
+   :ref:`MOEA\D <nb_moead>`, MOEAD, many,, Another well-known multi-objective optimization algorithm based on decomposition.
+   :ref:`AGE-MOEA <nb_agemoea>`, AGEMOEA, many,, Similar to NSGA-II but estimates the shape of the Pareto-front to compute a score replacing the crowding distance.
+   :ref:`C-TAEA <nb_ctaea>`, CTAEA, many, x, An algorithm with a more sophisticated constraint-handling for many-objective optimization algoritms.
+   :ref:`SMS-EMOA <nb_sms>`, CTAEA, many, x, An algorithm that uses hypervolume during the environmental survival.
+   :ref:`RVEA <nb_rvea>`, RVEA, many, x, A reference direction based algorithm used an angle-penalized metric.

docs/source/algorithms/list.ipynb

@@ -3,6 +3,9 @@
   {
    "cell_type": "raw",
    "metadata": {
+    "pycharm": {
+     "name": "#%% raw\n"
+    },
     "raw_mimetype": "text/restructuredtext"
    },
    "source": [
@@ -11,20 +14,27 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "# List Of Algorithms"
    ]
   },
   {
    "cell_type": "raw",
    "metadata": {
+    "pycharm": {
+     "name": "#%% raw\n"
+    },
     "raw_mimetype": "text/restructuredtext"
    },
    "source": [
     ".. csv-table:: Algorithms available in pymoo\n",
-    "   :header: \"Algorithm\", \"Class\", \"Convenience\", \"Objective(s)\", \"Constraints\", \"Description\"\n",
-    "   :widths: 60, 10, 10, 10, 10, 200\n",
+    "   :header: \"Algorithm\", \"Class\", \"Objective(s)\", \"Constraints\", \"Description\"\n",
+    "   :widths: 60, 10, 10, 10, 200\n",
     "   :file: algorithms.csv\n"
    ]
   }
@@ -59,4 +69,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/algorithms/moo/age.ipynb

@@ -148,7 +148,7 @@
    ],
    "source": [
     "from pymoo.algorithms.moo.age import AGEMOEA\n",
-    "from pymoo.factory import get_problem\n",
+    "from pymoo.problems import get_problem\n",
     "from pymoo.operators.crossover.pntx import TwoPointCrossover\n",
     "from pymoo.operators.mutation.bitflip import BitflipMutation\n",
     "from pymoo.operators.sampling.rnd import BinaryRandomSampling\n",

docs/source/algorithms/moo/nsga2.ipynb

@@ -186,7 +186,7 @@
    ],
    "source": [
     "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
-    "from pymoo.factory import get_problem\n",
+    "from pymoo.problems import get_problem\n",
     "from pymoo.operators.crossover.pntx import TwoPointCrossover\n",
     "from pymoo.operators.mutation.bitflip import BitflipMutation\n",
     "from pymoo.operators.sampling.rnd import BinaryRandomSampling\n",

docs/source/algorithms/moo/rnsga2.ipynb

@@ -99,7 +99,7 @@
     "import numpy as np\n",
     "\n",
     "from pymoo.algorithms.moo.rnsga2 import RNSGA2\n",
-    "from pymoo.factory import get_problem\n",
+    "from pymoo.problems import get_problem\n",
     "from pymoo.optimize import minimize\n",
     "from pymoo.visualization.scatter import Scatter\n",
     "\n",

docs/source/algorithms/moo/unsga3.ipynb

@@ -69,7 +69,7 @@
     "\n",
     "from pymoo.algorithms.moo.nsga3 import NSGA3\n",
     "from pymoo.algorithms.moo.unsga3 import UNSGA3\n",
-    "from pymoo.factory import get_problem\n",
+    "from pymoo.problems import get_problem\n",
     "from pymoo.optimize import minimize\n",
     "\n",
     "problem = get_problem(\"ackley\", n_var=30)\n",

docs/source/algorithms/soo/ga.ipynb

@@ -77,7 +77,7 @@
    ],
    "source": [
     "from pymoo.algorithms.soo.nonconvex.ga import GA\n",
-    "from pymoo.factory import get_problem\n",
+    "from pymoo.problems import get_problem\n",
     "from pymoo.optimize import minimize\n",
     "\n",
     "problem = get_problem(\"g1\")\n",

docs/source/algorithms/soo/isres.ipynb

@@ -67,7 +67,7 @@
    ],
    "source": [
     "from pymoo.algorithms.soo.nonconvex.isres import ISRES\n",
-    "from pymoo.factory import get_problem\n",
+    "from pymoo.problems import get_problem\n",
     "from pymoo.optimize import minimize\n",
     "\n",
     "problem = get_problem(\"g1\")\n",

docs/source/customization/binary.ipynb

@@ -98,4 +98,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/customization/custom.ipynb

@@ -422,4 +422,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/source/customization/initialization.ipynb

@@ -93,7 +93,7 @@
     "import numpy as np\n",
     "\n",
     "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
-    "from pymoo.factory import get_problem\n",
+    "from pymoo.problems import get_problem\n",
     "from pymoo.optimize import minimize\n",
     "\n",
     "problem = get_problem(\"zdt2\")\n",
@@ -169,7 +169,7 @@
     "import numpy as np\n",
     "\n",
     "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
-    "from pymoo.factory import get_problem\n",
+    "from pymoo.problems import get_problem\n",
     "from pymoo.core.evaluator import Evaluator\n",
     "from pymoo.core.population import Population\n",
     "from pymoo.optimize import minimize\n",

docs/source/interface/callback.ipynb

@@ -111,7 +111,7 @@
     "import numpy as np\n",
     "\n",
     "from pymoo.algorithms.soo.nonconvex.ga import GA\n",
-    "from pymoo.factory import get_problem\n",
+    "from pymoo.problems import get_problem\n",
     "from pymoo.core.callback import Callback\n",
     "from pymoo.optimize import minimize\n",
     "\n",

docs/source/interface/result.ipynb

@@ -38,7 +38,7 @@
    "outputs": [],
    "source": [
     "from pymoo.algorithms.soo.nonconvex.ga import GA\n",
-    "from pymoo.factory import get_problem\n",
+    "from pymoo.problems import get_problem\n",
     "from pymoo.optimize import minimize\n",
     "\n",
     "\n",