diff --git a/notebooks/NSGAIINotebook.ipynb b/notebooks/NSGAIINotebook.ipynb
index a97d99bd..ba39c6d5 100644
--- a/notebooks/NSGAIINotebook.ipynb
+++ b/notebooks/NSGAIINotebook.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
     "        problem=problem,\n",
     "        population_size=100,\n",
     "        offspring_population_size=100,\n",
-    "        mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
+    "        mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables(), distribution_index=20),\n",
     "        crossover=SBXCrossover(probability=1.0, distribution_index=20),\n",
     "        termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),\n",
     ")"
@@ -76,7 +76,19 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2024-01-22 12:06:30,420] [jmetal.core.algorithm] [DEBUG] Creating initial set of solutions...\n",
+      "[2024-01-22 12:06:30,429] [jmetal.core.algorithm] [DEBUG] Evaluating solutions...\n",
+      "[2024-01-22 12:06:30,436] [jmetal.core.algorithm] [DEBUG] Initializing progress...\n",
+      "[2024-01-22 12:06:30,437] [jmetal.core.algorithm] [DEBUG] Running main loop until termination criteria is met\n",
+      "[2024-01-22 12:06:33,973] [jmetal.core.algorithm] [DEBUG] Finished!\n"
+     ]
+    }
+   ],
    "source": [
     "algorithm.run()\n",
     "front = get_non_dominated_solutions(algorithm.get_result())"
@@ -123,22 +135,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f90a29a5e80>"
+       "<matplotlib.legend.Legend at 0x10ff7e640>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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FPfPMM0pOTlbbtm118803a9WqVWrRokWR79G0aVNt3LhRDodDN910kzp06KDHH39c9erVk9lc9NdpWa+70Pvvv6/u3btr4MCBateuncaNG+dKjjp27KhPP/1Ue/bsUe/evV0DBU2bNi35TSqCyWTShx9+qMsuu0zXXnut4uPj1bJlSy1ZsqRU7zNmzBhZLBa1a9dOl19+udvgRXkzGcYlrK8LQPn5+apbt66OHj2qOnXqlNv7pqWl6aGHHpJ07hchNTVVNput3N4fAHzt5MmTOnDggNt+J0BFKu53rjTf3wG1z01lZbfb3batNgxDiYmJstvtfowKAICqieSmHOzdu9dj7tDhcDA9BQCAH5DclIOYmBiPeVOLxVIhVesAAKB4JDflwGq1KiUlxfXcZDIpOTn5kg5bAwAAZUNyU05sNptrV0rDMDRhwgSlpaX5OSoAAKoekptyYrfbtXXrVtdzp9NJUTEAAH5AclNO9u7d69FGUTEAAL5HclNOYmJiPNrMZjNFxQAA+BjJTQWqYvsjAgBQKZDclBNv01KGYTAtBQCAj5HclBP2ugGAymPo0KEymUyaMmWKW/sHH3wgk8nkep6amqpOnTqpVq1aqlevnrp06aLk5GS3a/Lz8/XMM8/oqquuUvXq1dWgQQN1795d06ZN02+//ebx2YsWLZLFYtGIESM8Xvvkk09kMpl05MgRr89RPkhuysmFe92YzWa99dZb7HUDAH4SHh6uqVOnek1AJGnu3Ll6/PHHNWrUKO3YsUMbN27UuHHjVFBQ4Orz66+/qmfPnnrnnXc0ZswYffnll9q2bZtefPFFbd++XQsXLvR437S0NI0bN06LFi3SyZMnK+znQ9FC/B1AMLHZbFqzZo2WLVumJ554goMzAcCP4uPjtW/fPiUnJ2vatGker3/00UcaMGCA29/qq666yq3PpEmTlJ2drT179ridsB0dHa2bbrrJo7bywIED+uKLL/T+++8rPT1dy5cv13333VfOPxkuhpGbctajRw9J0rZt29jjBgD+P7vdrvT0dJ/+XbRYLHrppZf02muvef3cxo0ba9OmTcrKyvJ6vdPp1JIlS/TAAw+4JTbnO3+KS5Leeecd3Xbbbapbt64eeOABNnP1E5Kbclb4P1B6erqio6P5xQZQ5aWlpSk6OlrXX3+9z/8u3nXXXercubMmT57s8drkyZNVr149NW/eXG3atNHQoUO1dOlS10HIP//8s44cOaI2bdq4Xde1a1fVqlVLtWrV0sCBA13tTqdT8+bN0wMPPCBJuvfee/X555/rwIEDFfgTwhuSm3Jkt9v12muvuZ6zSzGAqs5ut2v48OGuhMEffxenTp2q+fPna9euXW7tTZo0UUZGhr755huNHj1aZ8+e1ZAhQ3TzzTe74vVmxYoV2rFjh/r166cTJ0642tevX6/jx4/r1ltvlSQ1bNhQN954o+bOnVsxPxiKRHJTjvbu3esx/8ouxQCqsr1793okCr7+u3jttdeqX79+mjhxotfX27dvrz//+c9asGCB1q9fr/Xr1+vTTz/V5Zdfrnr16mn37t1u/aOiotS6dWvVrl3brT0tLU2//vqrqlevrpCQEIWEhGj16tWaP39+sckSyh/JTTliOTgAuKssfxenTJmif/7zn8rIyCi2X7t27SRJx48fl9ls1oABA7RgwQL99NNPxV73yy+/6MMPP9TixYu1Y8cO12P79u367bfftG7dunL7WXBxJDflqHA5eGGBmclkYjk4gCqt8O+ixWKRdC6x8cffxQ4dOuj+++/Xq6++6mp79NFH9cILL2jjxo3KysrSpk2bNHjwYF1++eWKi4uTJL300ktq1qyZevTooblz5+rrr7/W/v37tWLFCmVkZLh+rn/84x9q0KCBBgwYoPbt27senTp10q233kr9pY+xFLyc2Ww2mUwm2Ww2tWjRQi1btpTdbifBAVBl2Ww29evXT/v27VPr1q399vfw+eef15IlS1zP4+PjNXfuXL3xxhv65Zdf1LBhQ8XFxWnDhg1q0KCBJKlBgwbavHmzpk6dqpdfflkHDhyQ2WxWTEyMEhIS9Pjjj0s6t2fOXXfd5bF6SpLuvvtuDRo0SIcPH/bJzwnJZFSxA5Dy8/NVt25dHT16VHXq1KmQz8jLy1Pjxo1dz81ms1JSUtj3BkBAOXnypA4cOKAWLVooPDzc3+GgCijud640399MS1WAM2fOuD1n1RQAAL5DclMBvB2iyaopAAB8g+SmAsTExHjMu7JqCgAA3yC5qQBWq1UvvPCC6zmHaAIA4DskNxVk0qRJrqLiBQsWUEwMIGBVsXUn8KPy+l0juakgJpNJN910kyRp5cqVFBMDCDjVqlWTJP3+++9+jgRVxenTpyXJtX9QWbHPTQUq/I+zcOFCLV68mOXgAAKKxWJRvXr1dOjQIUlSjRo1vO7jApQHp9Opn3/+WTVq1FBIyKWlJ+xzU0HsdruioqLchtgsFosyMzOpvQEQMAzDUG5uro4cOeLvUFAFmM1mtWjRQqGhoR6vleb7m5GbClLcIZokNwAChclkUpMmTdSoUSOPPbyA8hYaGupxFllZkNxUkMLD4s4/CZbl4AAClcViueQ6CMBXKCiuIBceoimdG7lZu3atH6MCACD4kdxUoH79+nlMTXEMAwAAFYvkpgJxDAMAAL5HclOBCutuzmc2m6m7AQCgApHcVKDCupvzE5zhw4ezWgoAgApEclPBbDabsrKyNHDgQElSVlaW0tPTqbsBAKCCsImfj/z3v/9V586dXc/NZjM7FgMAUEKl+f5m5MZH6tev7/bc6XSycgoAgApAcuMj3lZIsXIKAIDyR3LjIzExMR4HzrFjMQAA5Y/kxkesVqtSU1Ndz00mk9566y1WTgEAUM5IbnzIZrNp/vz5kqTatWvLarVScwMAQDkjufGx+++/X/Xr11d+fr5uvvlmRUdHKy0tzd9hAQAQNEhufOzgwYP69ddfXc9ZNQUAQPkiufExzpsCAKBikdz4WFHnTdWsWdNPEQEAEFxIbnzM23lTTqdTPXv2pPYGAIBywPELfrJlyxb16NHDrc1isSgzM5Pl4QAAXIDjFwJAQUGBRxu1NwAAXDqSGz/xtmOxdG5EBwAAlB3JjZ9YrVZNmzbNo33ixIksCwcA4BKQ3PhR165dPdqYmgIA4NKQ3PiRt2XhJpOJZeEAAFwCkhs/8rYs3DAMloUDAHAJ/J7czJkzR82bN1d4eLhiY2O1efPmYvvPmjVLbdq0UfXq1RUZGaknnnhCJ0+e9FG05c9ms2nTpk1uxcUcyQAAQNn5NblZsmSJkpKSNHnyZG3btk2dOnVSv379dOjQIa/9Fy5cqAkTJmjy5MnatWuX0tLStGTJEk2aNMnHkZevgoICXbjdELU3AACUjV+Tm5kzZ+rhhx/WsGHD1K5dO7355puqUaOG5s6d67X/F198oWuuuUb33XefmjdvrptuukkDBw686GhPZVfUkQytW7f2U0QAAAQuvyU3p0+f1tatWxUfH/+/YMxmxcfHKyMjw+s1vXr10tatW13JzA8//KDVq1fr1ltv9UnMFcVb7c3dd9/NTsUAAJSB35Kbw4cPy+FwKCIiwq09IiJCubm5Xq+577779Pzzz+sPf/iDqlWrplatWqlv377FTkudOnVK+fn5bo/KyGazKSsrS2PGjJEk/fvf/9bKlSupuwEAoJT8XlBcGp988oleeuklvf7669q2bZuWL1+uVatW6YUXXijymuTkZNWtW9f1iIyM9GHEpWO1WpWcnKzGjRvrt99+U//+/RUdHc3KKQAASsFvB2eePn1aNWrU0LJly3TnnXe62ocMGaIjR47oww8/9Limd+/e6tmzp15++WVX24IFCzR8+HAVFBR41K1I50ZuTp065Xqen5+vyMhIvx+cWRS73a6oqCi3AmMO1AQAVHUBcXBmaGiounbtqg0bNrjanE6nNmzYoLi4OK/X/P777x4JjMVikSSP1UaFwsLCVKdOHbdHZbZ3716vK6eKqkMCAADuQvz54UlJSRoyZIi6deumHj16aNasWTp+/LiGDRsmSRo8eLCaNWum5ORkSVL//v01c+ZMdenSRbGxsdq3b5+eeeYZ9e/f35XkBLrClVNOp9Ot/d5771V+fr5sNpufIgMAIDD4NblJSEjQzz//rGeffVa5ubnq3Lmz1qxZ4yoyzs7Odhupefrpp2UymfT000/rxx9/1OWXX67+/fvrxRdf9NePUO4KV04NHz7cLcEp3NivX79+TE8BAFAMv9Xc+Etp5uz8aenSpUpISPDafs899/ghIgAA/Ccgam5QvF69enktkE5ISGD1FAAAxSC5qaS8bewnnSuc5twpAACKRnJTidlsNi1atMijnXOnAAAoGslNJedteopzpwAAKBrJTSXnbXpq4MCBrJgCAKAIJDcBoPDcqZEjR0qSVq5cqW3btik9PZ3aGwAALsBS8ADicDgUGxurrVu3utrMZrNSUlLY3A8AENRYCh6kLBaL/vrXv7q1FW7uxwgOAADnkNwEmLCwMI82Vk8BAPA/JDcBJiYmRiaTya3NbDbr0KFDjN4AACCSm4BjtVqVmprqtnrK6XQqISFB0dHR7F4MAKjySG4CUOHqqbvvvtutnfobAABIbgKW1WrV8OHDPdqpvwEAVHUkNwGsXbt2HvU3FouF3YsBAFUayU0AK6y/OT/B+ctf/sLuxQCAKo3kJsDZbDZlZmbq6quvliQtX75cP/zwA7sXAwCqLJKbIBAVFaWPPvpI9evX17Zt29SqVStdf/31rJ4CAFRJJDdBolmzZpoyZYpbG6unAABVEclNEPFWSMzqKQBAVUNyE0SK2r2Y1VMAgKqE5CaIeNu9+JZbbmH1FACgSiG5CTKFuxc//fTTkqRVq1bpH//4B6unAABVhskwDMPfQfhSfn6+6tatq6NHj6pOnTr+DqdC/fnPf9Ybb7zhem42m5WSkiKbzebHqAAAKL3SfH8zchPEnnzySbfnrJ4CAFQFJDdBLDs726ON1VMAgGBHchPEYmJi3IqLJclkMqlmzZrU4AAAghbJTRCzWq1KSUmRxWJxa4+NjWUHYwBA0CK5CXKFZ099/PHHio+Pl2EYKqwhpwYHABCMQvwdACqe1WqV1WrViRMn9O9//9vttcIaHPbCAQAEC0ZuqpCOHTuygzEAIOiR3FQh3nYwvv3223Xw4EHNnDlTW7Zs8WN0AACUDzbxq4LsdrtmzJihWbNmebw2ZMgQzZs3z+cxAQBQHDbxQ7GsVqv+9re/6b777vN4bf78+YzgAAACGslNFdalSxev7Rs3bvRxJAAAlB+SmyqsT58+XtuvueYaH0cCAED5Ibmpwrp3764hQ4a4tUVERKhBgwbsYAwACFgkN1XcvHnztHnzZo0dO1Y1a9ZUXl6eWrVqxQ7GAICAxWopuLz77rt64IEH3NrMZrM2bdqk7t27+ykqAABYLYUyatq0qUeb0+lUz549GcEBAAQMkhu4eDtFXOIMKgBAYCG5gUvhKeLeEhyHw6GMjAw/RAUAQOmQ3MCNzWbTpk2bPM6gkqSEhASmpwAAlR7JDTx0797d4wwqSTIMg+kpAEClR3IDr2w2mxYtWuTR7nA4tG/fPj9EBABAyZDcoEi9evXyWn/z22+/sckfAKDSIrlBkQoLjC0Wi1v7n/70Jzb5AwBUWmzih4uy2+3at2+fTpw4oVtvvdXtNYvFoszMTFmtVj9FBwCoCtjED+XKarWqb9++Cg8P93iNGhwAQGVDcoMSK2qTv7Nnz1KDAwCoNEhuUGJF1eDceOON1OAAACoNam5QaoU1OHl5ebr33nvdXqMGBwBQEai5QYUqrMFp1KiRx2vU4AAA/I3kBmVWVA3OmTNnqMEBAPgNyQ3KrKganJtuuokaHACA31Bzg0tWWINz+PBh3XPPPW6vUYMDACgP1NzApwprcBo0aODxGjU4AABf83tyM2fOHDVv3lzh4eGKjY3V5s2bi+1/5MgRjRgxQk2aNFFYWJiuuOIKrV692kfRojhF1eBcOG0FAEBF8mtys2TJEiUlJWny5Mnatm2bOnXqpH79+unQoUNe+58+fVo33nijMjMztWzZMu3evVupqalq1qyZjyOHN0XV4Nxzzz36+uuv/RQVAKCq8WvNTWxsrLp3767Zs2dLkpxOpyIjIzVy5EhNmDDBo/+bb76pl19+Wd9//72qVatWps+k5qbiFdbg1KtXT8OGDdOOHTt02WWX6V//+pdiY2P9HR4AIAAFRM3N6dOntXXrVsXHx/8vGLNZ8fHxysjI8HrNRx99pLi4OI0YMUIRERFq3769XnrpJTkcDl+FjRIorMHp3Lmz0tPTFRcXp99++03x8fH65JNP/B0eACDI+S25OXz4sBwOhyIiItzaIyIilJub6/WaH374QcuWLZPD4dDq1av1zDPPaMaMGfrrX/9a5OecOnVK+fn5bg/4Tr169bRu3TrdcMMNKigo0C233KJVq1b5OywAQBDze0FxaTidTjVq1EgpKSnq2rWrEhIS9NRTT+nNN98s8prk5GTVrVvX9YiMjPRhxJCkWrVqaeXKlbr99tt18uRJ3XHHHZo/f77sdjub/QEAyp3fkpuGDRvKYrEoLy/PrT0vL0+NGzf2ek2TJk10xRVXuBWstm3bVrm5uTp9+rTXayZOnKijR4+6Hjk5OeX3Q6DEwsPDtWzZMj3wwANyOBwaOnSooqKi2OwPAFDu/JbchIaGqmvXrtqwYYOrzel0asOGDYqLi/N6zTXXXKN9+/bJ6XS62vbs2aMmTZooNDTU6zVhYWGqU6eO2wP+Ua1aNc2fP1+JiYmSpMJadqfTqcTEREZwAADlwq/TUklJSUpNTdX8+fO1a9cuPfroozp+/LiGDRsmSRo8eLAmTpzo6v/oo4/q119/1ejRo7Vnzx6tWrVKL730kkaMGOGvHwGlZDablZCQ4NHOZn8AgPIS4s8PT0hI0M8//6xnn31Wubm56ty5s9asWeMqMs7OznbbFC4yMlJr167VE088oY4dO6pZs2YaPXq0xo8f768fAWVQuNnf+SNwkjyKywEAKAvOloJfpKWlKTEx0W0Zf5cuXZSWlqYjR44oJiaG86gAAC6l+f4muYHfFG72VzgV+fPPP7teM5vNSklJkc1m82OEAIDKguSmGCQ3ldNnn32ma6+91q2NE8UBAIVK8/3t15oboNDZs2c92hwOhzIyMtSwYUOmqQAAJRZQm/gheBV1onhCQgJ74QAASoXkBpVCUSeKX7gXztKlS9kPBwBQLGpuUKkUFhkfOnTI6344EsXGAFAVUVBcDJKbwGC32xUdHe2xF04hio0BoGopzfc301KolIqapirEjsYAgKKQ3KDSstlsyszM1NKlSz1eM5lMat26NSeLAwA8kNygUrNarbrnnnv09ttvu62mMgxDgwcP5mRxAIAHam4QMOx2u/bu3au1a9dq2rRpuvBXlzocAAhebOKHoGS1WmW1WnXdddepVq1aeuaZZ9xeL6zDIbkBgKqNaSkEpKFDh8pkMrm1mc1mtW7d2k8RAQAqC5IbBCSr1arU1FSPOpwFCxbIMAwKjQGgCqPmBgHNbrfr+++/1/z587VgwQJJUteuXbVt2zYZhsGGfwAQJNjErxgkN8Hrrbfe0siRI3XmzBm3drPZrE2bNql79+5+igwAcKnYxA9VUmJiov72t795tDudTvXs2ZOl4gBQRTByg6Bit9sVFRXlsUxc+t9ScUnau3evYmJiWFkFAAGCkRtUWYWFxheupJLOLRV/5ZVXFB0dzcZ/ABDEGLlBUNqyZYt69uxZ5MGbhdj4DwACAyM3qPK6d+9e7MGbhTiAEwCCD8kNglbhwZvp6en69ttvdcMNN3jtt2XLFh9HBgCoSExLocpwOp364x//qH/9619u7UxNAUDlV2HTUidOnNDnn3+unTt3erx28uRJ/f3vfy9dpIAPmc1mjR071qOdqSkACC4lTm727Nmjtm3b6tprr1WHDh3Up08fHTx40PX60aNHNWzYsAoJEigvMTExbkc2FEpJSdGJEyf8EBEAoLyVOLkZP3682rdvr0OHDmn37t2qXbu2rrnmGmVnZ1dkfEC5slqtboXGJpNJJpNJixYtUs+ePbVnzx4/RwgAuFQlTm6++OILJScnq2HDhmrdurX++c9/ql+/furdu7d++OGHiowRKFfnFxpnZ2dr3bp1uvzyy/X111+ra9euWrJkib9DBABcghInNydOnFBISIjruclk0htvvKH+/furT58+/IsXAcVqtapv376yWq2Kj4/Xjh07dO2116qgoED33nuvHnnkEf3++++cLg4AAajEyc2VV16pr776yqN99uzZuuOOO3T77beXa2CALzVt2lQbNmzQxIkTJZ07hDMmJkZRUVHsZgwAAaZEyc3XX3+t22+/XYsWLfL6+uzZszVw4ECv5/kAgSIkJEQvvfSS1q1bp0aNGumnn35y/U47nU4lJiYyggMAAaBE+9xYLBYdPHhQjRo1UsuWLbVlyxY1aNDAF/GVO/a5QUksX75cd999t0d7enq6+vbt6/uAAKCKK/d9burVq6cDBw5IkjIzMy96Xg8Q6Hr06OH18M2ff/7ZD9EAAEoj5OJdpLvvvlvXXnutmjZtKpPJpG7duhV5Zg8rpxAMCk8XT0xMlMPhcLUnJCTo66+/1uTJk90K7AEAlUeJj19Ys2aN9u3bp1GjRun5559X7dq1vfYbPXp0uQZY3piWQmnY7Xbt27dPTZo00bRp0zR37lxJUlxcnBYuXKjmzZvLbrdr7969iomJ4QgHAKggpfn+LvXZUsOGDdOrr75aZHJT2ZHc4FIsXrxYiYmJys/PV506dXTPPfdo7ty5MgxDZrNZKSkpstls/g4TAIJOhSY3gY7kBpfqwIEDuu+++7Rp0yaP1ziEEwAqRoUdnAlAatGihT777DOvZ6lxCCcA+B/JDVAGISEhev755z1WVJlMJjVr1oydjQHAj0hugDIqXFF1/injhmHo2muvZWdjAPAjam6AS1S4oionJ0djxozRoUOH3F63WCxauHChevXqRS0OAJRRab6/2agDuERWq9WVtNSuXVt33XWX2+sOh0MJCQmspgIAH2FaCihH3bp187qzscT5VADgKyQ3QDkqrMMpagdvVlMBQMUjuQHKmc1mU2ZmppYuXer19c2bN6uKlboBgE+R3AAVwGq16p577tHbb7/tMYozfvx43XzzzcrJyWHJOABUAFZLARWscDVVixYttHz5ck2aNEknT55U9erVdfLkSY5uAIAS4PiFYpDcwN92796t++67T9u2bXNrN5vNWrRoEUvGAcALjl8AKrE2bdpo6tSpHu1Op1MJCQls/AcAl4jkBvCDK6+80m1n4/OxZBwALg3JDeAHVqtVKSkpxS4Z37hxI8XGAFAG1NwAfmS325WRkaF7771XTqfTax+KjQGAguJikdygMkpLS1NiYqIcDodMJpPHPjhms1mbNm1SQUGBYmJiKDgGUOWQ3BSD5AaVVeGS8UOHDikhIaHIfozkAKiKSG6KQXKDys5utys6OrrIaSrp3EnjmZmZjOAAqDJYCg4EsAuLjb0dxFl4RhU7HAOAJ0ZugEqqcJqqZs2a6tmzp8dITvfu3fXVV1+xwzGAKoFpqWKQ3CAQXazgWGKqCkBwC7hpqTlz5qh58+YKDw9XbGysNm/eXKLrFi9eLJPJpDvvvLNiAwT8rPCk8fT0dGVnZ+vVV1/16FM4VQUAVZ3fk5slS5YoKSlJkydP1rZt29SpUyf169dPhw4dKva6zMxMjRkzRr179/ZRpIB/Wa1W9e3bV1arVXfddZfXWpyVK1fq1KlTkkQ9DoAqy+/JzcyZM/Xwww9r2LBhateund58803VqFFDc+fOLfIah8Oh+++/X3/5y1/UsmVLH0YLVA5Wq1WpqakeOxzPmDFDnTp10rhx4xQVFaXrr79eUVFRnFUFoErxa3Jz+vRpbd26VfHx8a42s9ms+Ph4ZWRkFHnd888/r0aNGpWoePLUqVPKz893ewDB4MKpqkWLFikiIkK7d+/Wyy+/7KrLMQxDDz/8MCM4AKoMvyY3hw8flsPhUEREhFt7RESEcnNzvV7z+eefKy0tTampqSX6jOTkZNWtW9f1iIyMvOS4gcqicKoqMjJS9957r77//nvdeOONHv0Mwyj2HwwAEEz8Pi1VGseOHdOgQYOUmpqqhg0bluiaiRMn6ujRo65HTk5OBUcJ+E+9evX00EMPeX2t8HefWhwAwS7Enx/esGFDWSwW5eXlubXn5eWpcePGHv3379+vzMxM9e/f39VWuPdHSEiIdu/erVatWrldExYWprCwsAqIHqicevXq5XW5+Pjx47Vu3TqtW7eOvXEABDW/jtyEhoaqa9eu2rBhg6vN6XRqw4YNiouL8+h/5ZVX6ptvvtGOHTtcj9tvv13XXXedduzYwZQTIM9iY7PZrC5duujs2bNau3atK+lxOp1KTExkBAdA0PHryI0kJSUlaciQIerWrZt69OihWbNm6fjx4xo2bJgkafDgwWrWrJmSk5MVHh6u9u3bu11fr149SfJoB6oym82mfv36ad++fWrdurWsVquSk5M1adIkt36Fe+Ow8R+AYOL35CYhIUE///yznn32WeXm5qpz585as2aNq8g4OztbZnNAlQYBlYLVanVLWgYNGqSnnnrKY7pq6dKl6tatm2rVquXrEAGgQnD8AlCFnH+Mw/msVqtmzpyp//u///O6OSAA+FvAHb8AwDfO3xsnJydH//znP9WiRQvZ7XYNGDBAN910k3bv3u3qz8oqAIGIkRugijtx4oSmTp2qKVOm6NSpU6pWrZqefPJJWa1WjRo1Sk6nUyaTSVOnTtXYsWP9HS6AKopTwYtBcgN4t3//fo0aNUqrV68uss/LL7+sMWPG+DAqADiHaSkApdaqVSutXLlSH374oZo2beq1z/jx45miAlDp+X21FIDKw2Qy6fbbb9dVV12l1q1be7zudDq1b98+SdLevXsVExPDMnIAlQ4jNwA8tGrVStOmTfP62ssvv+w6cTw6OpoTxwFUOtTcACjS9OnTNX78eNcxJ95YLBZlZmYyggOgQlFzA6BcjBkzRllZWUpPT1dWVpbX1VKFuxwDQGVBcgOgWFarVX379lVUVJRGjRrldZO/RYsW6ejRo+yLA6BSYFoKQKkUtctxrVq1dPz4cdeJ41OmTFG3bt0oOgZQLtjnphgkN8Cls9vtrkM5v/nmG40cOVL79+/32tdsNislJUU2m83HUQIIJiQ3xSC5Acrf+vXrddNNNxX5OkXHAC5Vab6/2ecGwCVr27atzGZzkauqzi86Zn8cABWNgmIAl8xqtSolJUUWi6XIPlOnTmV/HAA+wbQUgHJTWIvz1VdfacKECR5Fx+djqgpAaVBzUwySG8A3ChOdli1bKiUlRS+++KJHn6VLl6phw4ZMUwG4KJKbYpDcAL5nt9sVFRWlov7cmM1mTZ06VV27diXRAeAVOxQDqFSsVqtSU1OLrMlxOp0aO3Ys9TgAygUjNwB8pnCq6tChQ0pISCiy3/n1OHa7nRVWAFgKDqByslqtroTlYkvHv//+e61du1bDhw+X0+lkM0AAJcbIDQC/KOoYh0JRUVHKyclxq9NhhRVQdVFzA6DSs9lsyszMVHp6uqZNm+aqxzGZTKpdu7ays7M9CpA5gRxASZDcAPCbwhPHx44d60p0srOzZbfbNXr0aI/+ZrNZrVu39kOkAAIJyQ2ASqEw0bFarapTp45mzZql6dOny2QyufoYhqHnnntOP/74o6RzBcrp6emy2+3+ChtAJUTNDYBKzW636+OPP9bixYv1r3/9S5JUvXp19enTR2vXrpVhGBQbA1UAm/gVg+QGCFxffPGFxo0bp40bN3q8RrExENwoKAYQlHr16qXPPvvM61EODodDO3fu9ENUACobkhsAAcVkMmnw4MFutTiFHnzwQb3zzjs6e/ZssfU41OoAwY3kBkDAufA4B5PJpMsuu0w//vijHnzwQUVGRioqKsrrcQ5paWmKjo7mqAcgiFFzAyBgFR7n0Lp1azVo0ECvv/66XnzxRf32229u/cxms7KysiRJ0dHRbjsjU6sDBAaOXwBQJRQe51DoySefVJs2bdS/f3+3fk6nUx988IGuuuoqjyMfCjcGJLkBggfJDYCg0rlzZ6/nVo0cOVI33HCDTCaTx5EObAwIBBdqbgAEFavVqpSUFFc9jtls1rXXXiuLxaINGzZ4HOnwwAMPMGoDBBlqbgAEpfPrcaxWq/bu3auxY8fqww8/dOt3fj3O3r17FRMTQ7IDVEJs4lcMkhug6kpPT9f111/v0d6uXTvt2rWL3Y6BSoxN/ADAi5iYGJnNnn/2du7c6ZqucjqdSkxMZA8cIICR3ACoMi6sx7FYLHrggQc8+jkcDq9HPAAIDExLAahyzq/HkTz3vpGkatWq6eGHH9akSZPUrFkzf4QJ4DxMSwFAMaxWq/r27evaJ+fC1VVXXnmlzpw5o9dff12tWrXS448/rtzcXD9HDaCkSG4AVHk2m02ZmZlKT09XVlaWdu3apfT0dP3hD3/QqVOn9Morr6hly5YaM2aM8vLyXNdxRhVQOTEtBQBFMAxDGzZs0DPPPKNNmzZJkqpXr67ExERZrVaNGzdOTqdTJpNJTz75pEaPHs0ycqCCsBS8GCQ3AErLMAytWbNGf/nLX/Tll18W2Y9l5EDFIbkpBskNgLIyDEPr169XUlKSvvvuO699LBaLMjIyVFBQwIaAQDkiuSkGyQ2AS5WTk6Po6GiPoxwKFZ5fZTablZSUxHQVUA5YLQUAFSgyMlKpqaleNwSU5LYh4PTp0xUVFaW0tDRfhghUaSQ3AFAGNptNWVlZGjNmjGsZeVEMw2DXY8CHSG4AoIysVqtefvll1zLyzZs3y2Qyee3rcDi0b98+H0cIVE0kNwBwiQo3BezevbtSU1OLHMmZOXOmvvnmG0nskQNUJJIbAChHhRsCjhkzxqMm55///Kc6duyoLl26KCoqStdff72io6OpxwHKGaulAKCCnH+G1S+//KIXX3xRy5Yt81hlZTablZWVxYoqoBislgKASuD8M6w6deqkpUuX6p133vHo53Q69fe//73IpeUASofkBgB86IYbbvC6hPypp55S586dtXjxYjkcDo/XqdEBSo7kBgB8yNsp5DfffLNq166tr7/+WgMHDtSVV16ptLQ0nT59WpKUlpam6OhoanSAEqLmBgD84Px6HKvVqt9++02zZ8/WrFmz9Ouvv0o6lwg9/PDDeu6559ymrCwWizIzM6nRQZXC8QvFILkBUJkVFBQoJSVF06dP18GDB4vst3TpUjVs2JDzq1BlUFAMAAGqVq1aSkpK0oEDB/TWW28pKirKo4/JZFJCQgLTVEARKkVyM2fOHDVv3lzh4eGKjY3V5s2bi+ybmpqq3r1767LLLtNll12m+Pj4YvsDQCAKCwvT8OHDtX//fj300ENurxmG4XZ+VWJiorZs2ULBMfD/+T25WbJkiZKSkjR58mRt27ZNnTp1Ur9+/XTo0CGv/T/55BMNHDhQ6enpysjIUGRkpG666Sb9+OOPPo4cACpeSEiIUlNTlZWVpRdeeEFXXHGFRx+Hw6GePXsykgP8f36vuYmNjVX37t01e/ZsSef+FRIZGamRI0dqwoQJF73e4XDosssu0+zZszV48OCL9qfmBkAgy8nJUXR0dLF74lBwjGAUMDU3p0+f1tatWxUfH+9qM5vNio+PV0ZGRone4/fff9eZM2dUv359r6+fOnVK+fn5bg8ACFSRkZHFnl8lnftH3+7du9kbB1WWX5Obw4cPy+FwKCIiwq09IiJCubm5JXqP8ePHq2nTpm4J0vmSk5NVt25d1yMyMvKS4wYAfyo8v6q4k8gHDBjA+VWosvxec3MppkyZosWLF2vFihUKDw/32mfixIk6evSo65GTk+PjKAGg/BV1ErnJZFLNmjX166+/uhUdDx8+nBEcVBl+TW4aNmwoi8WivLw8t/a8vDw1bty42GunT5+uKVOmaN26derYsWOR/cLCwlSnTh23BwAEk/NHcrKzs/Xee+959HE6nXr88ceVnZ3thwgB3/JrchMaGqquXbtqw4YNrjan06kNGzYoLi6uyOumTZumF154QWvWrFG3bt18ESoAVGrnH9LZoUMHr+dXvf/++2rVqpUeeOABbd++3dVObQ6Cjd+npZKSkpSamqr58+dr165devTRR3X8+HENGzZMkjR48GBNnDjR1X/q1Kl65plnNHfuXDVv3ly5ubnKzc1VQUGBv34EAKhUvJ1f9cQTT+j666/X2bNn9e677+rqq6/WDTfcoNGjR1Obg6Dj96XgkjR79my9/PLLys3NVefOnfXqq68qNjZWktS3b181b95c8+bNkyQ1b95cWVlZHu8xefJkPffccxf9LJaCA6gqLjy/SpK2bt2qGTNmaOnSpV5PH7dYLMrIyFBBQQFHO6BS4WypYpDcAICUnZ2tMWPGeK3PMZlMMgxDZrNZKSkpstlsfogQcEdyUwySGwA4x263Kyoqig0BERACZhM/AID/WK1Wj2XkF3I4HFqxYoUrAaL4GIGAkRsAqOIKa3Nq1qyp2NhYryM5PXv2VOfOnfXWW28xZQW/YFqqGCQ3AFC0tLQ0JSYmyuFwyGw265prrtHmzZt16tQpj75MWcGXmJYCAJTJ+RsCZmVl6T//+Y+ysrI0aNAgj74Oh8N1DiDTVahMGLkBAFxUUcXHISEhuvrqq7Vlyxamq1ChGLkBAJSrC4uPzWaz2rRpo7Nnz2rz5s0e51ht2bKFkRz4DckNAKBELpyy+v7775WSkuLRz+l0KjY2ll2P4TdMSwEAysxutys6OlpOp7PIPmaz2bWz/N69e9n5GGXCtBQAwCcuPMfK2145TqdTvXv39jjDiiJkVBRGbgAAl+z8vXJ69uxZ7EiO9L8kiCJklBQjNwAAn7Jarerbt6+6d+/uNpJjsVj00EMPefQ3DMOtCDkxMZERHJQbRm4AAOXu/BPJJV20LkeSPv74Y1133XWy2+3U5sADIzcAAL8qHMmxWq0edTlms/evnscff1w2m82tNmf69Om+DBtBgpEbAIBPnD+as3btWtcxDyaTSRaLRWfPnvV63bRp0zR27FgfR4vKhrOlikFyAwCVw/nJTlhYmCZOnOh1TxyTyaSsrCxFRka6rmPaquohuSkGyQ0AVE5FHfEgSa1atdKECRN0+vRpjRw5Uk6nk1VWVQzJTTFIbgCg8po+fXqppqA4mbzqKM33d4iPYgIA4KLGjBkjwzA0YcIEOZ1OWSwWzZgxQ2fOnNGMGTOUm5vr1t/hcGjv3r2S2P0Y/8PIDQCg0jm/HqcwWcnKylKLFi08pq0aNWqkn3/+mQ0BgxzTUsUguQGAwJWWlua2yio0NFSnTp1y61N4lhUjOMGFfW4AAEHp/JPJs7Oz9d5773n0cTqd+tOf/qQPP/zQtbycc6yqFkZuAAAB62KnkkdGRqpr16768MMPmbYKcIzcAACqhAt3P7ZYLEpOTta4cePUoEED5eTk6IMPPnA7x2r48OGuERxGdIITIzcAgIDnrQD55MmT+stf/qIpU6Z49B89erRat26t0aNHs2dOgKCguBgkNwBQdVxs2up8FotFGRkZKigoYEl5JcS0FAAA8py2MpvNuvfee11HOZzP4XCoR48erkM7vR0FgcDAyA0AIOhdOG2Vk5Oj6Ohor0c9FLpw92POtPIvRm4AADiP1WpV3759XUlJZGSkUlNTXSM6JpPJ4xqHw6GFCxfK6XQqLS1N0dHRjOoECEZuAABVVuGITs2aNRUbG+t1JCc6OlrZ2dlur5nNZi1atEi9evViFMdHGLkBAKAECkd0unfv7jaSYzabdcMNN6hu3brKysrySHqcTqcSEhIYxamkGLkBAOD/u7A25/jx43rjjTeKPamck8l9g5EbAADK4MLanJo1a2rMmDF6++23ZTZ7/8p0OBxavny5DMNgU8BKgpEbAABKwG63KyMjQwkJCV5rc5o0aaLc3FyOeaggjNwAAFDOrFar7rnnHo/anN69e6t69eo6ePCgxzEPOTk5bu/ByI5vkNwAAFAK559MnpWVpf/85z9aunSpRz+n06k+ffpo9uzZOnLkCMvJfYhpKQAALtHFjnkIDw/XyZMn3do47qF0mJYCAMCHvJ1O/uqrr+q1115T+/btPRIb6Vwhcs+ePV0jOWPHjmW6qpwwcgMAQDnxdjq5YRj68MMPddddd130epPJpNTUVAqRveBU8GKQ3AAA/CEtLU3Dhw+/6Anl7JvjHdNSAABUMjabTVlZWUpPT9eXX37p9Twr6dx01eLFiy+aBKFojNwAAOAHaWlpSkxMlMPh8Pp6dHS0bDabhg0bJklV/kRypqWKQXIDAKgs7Ha7XnnlFc2cOVNOp1Nms1l9+/bVtm3bdOTIEUnn6nAKv6qrck0OyU0xSG4AAJXNhYXIJ06c0PLlyzVnzhxlZGS49TWZTPr000/Vu3dv17VVYVSH5KYYJDcAgECRnp6u66+/3utrffr0UUxMjNLS0qrEkQ8kN8UguQEABIqiNgc0m81eC46DeaUVq6UAAAgC3jYHfPvtt5WZmekqND6fw+HQzJkzXfU6VRUjNwAAVHLeNge02+2KioryekJ5eHi4/vSnP+nBBx/UddddJ7PZ7LomUOtzmJYqBskNACBYnL+c3Gw26//+7//03Xff6bvvvnP1iY6O1tChQ1W9enVNmjTJtSor0OpzSG6KQXIDAAgmF47qGIahr776Su+8844WLlyoo0ePer2usD5HCow9dEhuikFyAwCoKk6cOKEPPvhAM2bM0NatWz1ev//++7Vw4cKAWG1FclMMkhsAQFVTXH3O+SrzaitWSwEAABer1arU1FTXqiuz2ax27dp59HM4HJo3b57OnDnj6xDLFSM3AABUEefX50gqcjSnUaNGGjRokIYNG6arrrrKda0/a3OYlioGyQ0AAOdcuNrqxhtv1I4dO5SXl+fq06NHD7Vp00YLFizwa20OyU0xSG4AAPifC1dbnTlzRmvWrNHcuXO1cuVKnT171uMas9msrKwsn47gBFzNzZw5c9S8eXOFh4crNjZWmzdvLrb/e++9pyuvvFLh4eHq0KGDVq9e7aNIAQAILlarVX379nUlKtWqVVP//v21YsUK/fjjj3r00Uc9rnE6nZowYYL27Nnj63BLxO/JzZIlS5SUlKTJkydr27Zt6tSpk/r166dDhw557f/FF19o4MCBstls2r59u+68807deeed+vbbb30cOQAAwa1Ro0aaNGmSTCaTx2vvvvuu2rRpo169eiklJcV15IPdbld6errsdruPo/0fv09LxcbGqnv37po9e7akc9lgZGSkRo4cqQkTJnj0T0hI0PHjx7Vy5UpXW8+ePdW5c2e9+eabF/08pqUAACidC2tzhg8frpycHK1Zs0YOh0OSFBYWpk6dOmnLli0VUpsTMNNSp0+f1tatWxUfH+9qM5vNio+PV0ZGhtdrMjIy3PpLUr9+/Yrsf+rUKeXn57s9AABAydlsNmVmZio9PV1ZWVl64403tHLlStntdk2fPl1XXXWVTp06pc2bN7tWXzmdTiUmJvplBMevyc3hw4flcDgUERHh1h4REaHc3Fyv1+Tm5paqf3JysurWret6REZGlk/wAABUIRfW5khS48aN9eSTT+qbb77xOnvicDi0b98+X4YpqRLU3FS0iRMn6ujRo65HTk6Ov0MCACComEwm3Xbbba7TxwtZLBbXnjq+5NfkpmHDhrJYLG7r6SUpLy9PjRs39npN48aNS9U/LCxMderUcXsAAIDyZbValZKS4toF2WKx6K233vLLhn9+TW5CQ0PVtWtXbdiwwdXmdDq1YcMGxcXFeb0mLi7Orb8krV+/vsj+AADAN86vzcnMzPTbIZwhfvnU8yQlJWnIkCHq1q2bevTooVmzZun48eMaNmyYJGnw4MFq1qyZkpOTJUmjR49Wnz59NGPGDN12221avHixvvrqK6WkpPjzxwAAADo3guPvgzf9ntwkJCTo559/1rPPPqvc3Fx17txZa9ascRUNZ2dnu83h9erVSwsXLtTTTz+tSZMmKSYmRh988IHat2/vrx8BAABUIn7f58bX2OcGAIDAEzD73AAAAJQ3khsAABBUSG4AAEBQIbkBAABBheQGAAAEFZIbAAAQVEhuAABAUCG5AQAAQYXkBgAABBW/H7/ga4UbMufn5/s5EgAAUFKF39slOVihyiU3x44dkyRFRkb6ORIAAFBax44dU926dYvtU+XOlnI6nfrpp59Uu3ZtmUymcn3v/Px8RUZGKicnh3OrKhD32Te4z77BffYd7rVvVNR9NgxDx44dU9OmTd0O1Pamyo3cmM3mCj+KvU6dOvyP4wPcZ9/gPvsG99l3uNe+URH3+WIjNoUoKAYAAEGF5AYAAAQVkptyFBYWpsmTJyssLMzfoQQ17rNvcJ99g/vsO9xr36gM97nKFRQDAIDgxsgNAAAIKiQ3AAAgqJDcAACAoEJyAwAAggrJTSnNmTNHzZs3V3h4uGJjY7V58+Zi+7/33nu68sorFR4erg4dOmj16tU+ijSwleY+p6amqnfv3rrssst02WWXKT4+/qL/XXBOaX+fCy1evFgmk0l33nlnxQYYJEp7n48cOaIRI0aoSZMmCgsL0xVXXMHfjhIo7X2eNWuW2rRpo+rVqysyMlJPPPGETp486aNoA9N//vMf9e/fX02bNpXJZNIHH3xw0Ws++eQTXX311QoLC1Pr1q01b968Co9TBkps8eLFRmhoqDF37lzju+++Mx5++GGjXr16Rl5entf+GzduNCwWizFt2jRj586dxtNPP21Uq1bN+Oabb3wceWAp7X2+7777jDlz5hjbt283du3aZQwdOtSoW7euYbfbfRx5YCntfS504MABo1mzZkbv3r2NO+64wzfBBrDS3udTp04Z3bp1M2699Vbj888/Nw4cOGB88sknxo4dO3wceWAp7X1+9913jbCwMOPdd981Dhw4YKxdu9Zo0qSJ8cQTT/g48sCyevVq46mnnjKWL19uSDJWrFhRbP8ffvjBqFGjhpGUlGTs3LnTeO211wyLxWKsWbOmQuMkuSmFHj16GCNGjHA9dzgcRtOmTY3k5GSv/QcMGGDcdtttbm2xsbFGYmJihcYZ6Ep7ny909uxZo3bt2sb8+fMrKsSgUJb7fPbsWaNXr17G22+/bQwZMoTkpgRKe5/feOMNo2XLlsbp06d9FWJQKO19HjFihHH99de7tSUlJRnXXHNNhcYZTEqS3IwbN8646qqr3NoSEhKMfv36VWBkhsG0VAmdPn1aW7duVXx8vKvNbDYrPj5eGRkZXq/JyMhw6y9J/fr1K7I/ynafL/T777/rzJkzql+/fkWFGfDKep+ff/55NWrUSDabzRdhBryy3OePPvpIcXFxGjFihCIiItS+fXu99NJLcjgcvgo74JTlPvfq1Utbt251TV398MMPWr16tW699VafxFxV+Ot7sModnFlWhw8flsPhUEREhFt7RESEvv/+e6/X5Obmeu2fm5tbYXEGurLc5wuNHz9eTZs29fgfCv9Tlvv8+eefKy0tTTt27PBBhMGhLPf5hx9+0Mcff6z7779fq1ev1r59+/TnP/9ZZ86c0eTJk30RdsApy32+7777dPjwYf3hD3+QYRg6e/asHnnkEU2aNMkXIVcZRX0P5ufn68SJE6pevXqFfC4jNwgqU6ZM0eLFi7VixQqFh4f7O5ygcezYMQ0aNEipqalq2LChv8MJak6nU40aNVJKSoq6du2qhIQEPfXUU3rzzTf9HVpQ+eSTT/TSSy/p9ddf17Zt27R8+XKtWrVKL7zwgr9DQzlg5KaEGjZsKIvFory8PLf2vLw8NW7c2Os1jRs3LlV/lO0+F5o+fbqmTJmif//73+rYsWNFhhnwSnuf9+/fr8zMTPXv39/V5nQ6JUkhISHavXu3WrVqVbFBB6Cy/D43adJE1apVk8VicbW1bdtWubm5On36tEJDQys05kBUlvv8zDPPaNCgQXrooYckSR06dNDx48c1fPhwPfXUUzKb+bd/eSjqe7BOnToVNmojMXJTYqGhoeratas2bNjganM6ndqwYYPi4uK8XhMXF+fWX5LWr19fZH+U7T5L0rRp0/TCCy9ozZo16tatmy9CDWilvc9XXnmlvvnmG+3YscP1uP3223Xddddpx44dioyM9GX4AaMsv8/XXHON9u3b50oeJWnPnj1q0qQJiU0RynKff//9d48EpjChNDhysdz47XuwQsuVg8zixYuNsLAwY968ecbOnTuN4cOHG/Xq1TNyc3MNwzCMQYMGGRMmTHD137hxoxESEmJMnz7d2LVrlzF58mSWgpdAae/zlClTjNDQUGPZsmXGwYMHXY9jx47560cICKW9zxditVTJlPY+Z2dnG7Vr1zYee+wxY/fu3cbKlSuNRo0aGX/961/99SMEhNLe58mTJxu1a9c2Fi1aZPzwww/GunXrjFatWhkDBgzw148QEI4dO2Zs377d2L59uyHJmDlzprF9+3YjKyvLMAzDmDBhgjFo0CBX/8Kl4GPHjjV27dplzJkzh6XgldFrr71mREVFGaGhoUaPHj2MTZs2uV7r06ePMWTIELf+S5cuNa644gojNDTUuOqqq4xVq1b5OOLAVJr7HB0dbUjyeEyePNn3gQeY0v4+n4/kpuRKe5+/+OILIzY21ggLCzNatmxpvPjii8bZs2d9HHXgKc19PnPmjPHcc88ZrVq1MsLDw43IyEjjz3/+s/Hbb7/5PvAAkp6e7vXvbeG9HTJkiNGnTx+Pazp37myEhoYaLVu2NN55550Kj9NkGIy/AQCA4EHNDQAACCokNwAAIKiQ3AAAgKBCcgMAAIIKyQ0AAAgqJDcAACCokNwAAICgQnIDIGAZhqHhw4erfv36MplMnFgOQBLJDYAAtmbNGs2bN08rV67UwYMHlZ+fr/79+6tp06YymUz64IMP/B0iAD8guQEQsPbv368mTZqoV69eaty4sY4fP65OnTppzpw5/g4NgB+F+DsAACiLoUOHav78+ZIkk8mk6OhoZWZm6pZbbvFzZAD8jeQGQEB65ZVX1KpVK6WkpGjLli2yWCz+DglAJUFyAyAg1a1bV7Vr15bFYlHjxo39HQ6ASoSaGwAAEFRIbgAAQFAhuQEAAEGFmhsAQaOgoED79u1zPT9w4IB27Nih+vXrKyoqyo+RAfAlkhsAQeOrr77Sdddd53qelJQkSRoyZIjmzZvnp6gA+JrJMAzD30EAAACUF2puAABAUCG5AQAAQYXkBgAABBWSGwAAEFRIbgAAQFAhuQEAAEGF5AYAAAQVkhsAABBUSG4AAEBQIbkBAABBheQGAAAEFZIbAAAQVP4f/NrgPsXO1U0AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -153,7 +165,7 @@
     "\n",
     "plt.xlabel(\"f1\")\n",
     "plt.ylabel(\"f2\")\n",
-    "plt.title(problem.get_name())\n",
+    "plt.title(problem.name())\n",
     "plt.legend([\"Reference front\" , \"NSGAII\"])"
    ]
   },
@@ -166,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -186,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -210,9 +222,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2024-01-22 12:06:55,103] [jmetal.core.algorithm] [DEBUG] Creating initial set of solutions...\n",
+      "[2024-01-22 12:06:55,156] [jmetal.core.algorithm] [DEBUG] Evaluating solutions...\n",
+      "[2024-01-22 12:06:55,158] [jmetal.core.algorithm] [DEBUG] Initializing progress...\n",
+      "[2024-01-22 12:06:55,159] [jmetal.core.algorithm] [DEBUG] Running main loop until termination criteria is met\n",
+      "[2024-01-22 12:07:02,131] [jmetal.core.algorithm] [DEBUG] Finished!\n"
+     ]
+    }
+   ],
    "source": [
     "algorithm.run()\n",
     "front = get_non_dominated_solutions(algorithm.get_result())"
@@ -227,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,22 +263,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f90a2bc3b20>"
+       "<matplotlib.legend.Legend at 0x12d675eb0>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -264,14 +288,28 @@
     }
    ],
    "source": [
-    "plt.plot(x, y, color=\"black\")\n",
+    "plt.scatter(x, y, color=\"black\")\n",
     "\n",
     "plt.xlabel(\"f1\")\n",
     "plt.ylabel(\"f2\")\n",
-    "plt.title(problem.get_name())\n",
+    "plt.title(problem.name())\n",
     "plt.legend([\"NSGAII\"])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -296,9 +334,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/notebooks/SolvingConstrainedProblems.ipynb b/notebooks/SolvingConstrainedProblems.ipynb
index 16617f23..5cef31d2 100644
--- a/notebooks/SolvingConstrainedProblems.ipynb
+++ b/notebooks/SolvingConstrainedProblems.ipynb
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "id": "2fb8a05d",
    "metadata": {},
    "outputs": [],
@@ -99,17 +99,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "id": "14820ce9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<jmetal.core.problem.OnTheFlyFloatProblem at 0x7fde72368490>"
+       "<jmetal.core.problem.OnTheFlyFloatProblem at 0x107ad3eb0>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,26 +131,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "id": "1398092f",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'DominanceWithConstraintsComparator' from 'jmetal.util.comparator' (/Users/ajnebro/opt/anaconda3/envs/jmetalpy/lib/python3.9/site-packages/jmetal/util/comparator.py)",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mImportError\u001B[0m                               Traceback (most recent call last)",
-      "Cell \u001B[0;32mIn[13], line 3\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mjmetal\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01malgorithm\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mmultiobjective\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m NSGAII\n\u001B[1;32m      2\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mjmetal\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01moperator\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PolynomialMutation, SBXCrossover\n\u001B[0;32m----> 3\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mjmetal\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mutil\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mcomparator\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m DominanceWithConstraintsComparator\n\u001B[1;32m      5\u001B[0m algorithm \u001B[38;5;241m=\u001B[39m NSGAII(\n\u001B[1;32m      6\u001B[0m         problem\u001B[38;5;241m=\u001B[39mproblem,\n\u001B[1;32m      7\u001B[0m         population_size\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m100\u001B[39m,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m     11\u001B[0m         termination_criterion\u001B[38;5;241m=\u001B[39mStoppingByEvaluations(max_evaluations\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m25000\u001B[39m),\n\u001B[1;32m     12\u001B[0m         dominance_comparator\u001B[38;5;241m=\u001B[39mDominanceWithConstraintsComparator())\n",
-      "\u001B[0;31mImportError\u001B[0m: cannot import name 'DominanceWithConstraintsComparator' from 'jmetal.util.comparator' (/Users/ajnebro/opt/anaconda3/envs/jmetalpy/lib/python3.9/site-packages/jmetal/util/comparator.py)"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from jmetal.algorithm.multiobjective import NSGAII\n",
     "from jmetal.operator import PolynomialMutation, SBXCrossover\n",
     "from jmetal.util.comparator import DominanceWithConstraintsComparator\n",
+    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
+    "from jmetal.util.solution import get_non_dominated_solutions\n",
     "\n",
     "algorithm = NSGAII(\n",
     "        problem=problem,\n",
@@ -164,16 +154,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "9536cae3",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[2024-01-22 12:11:43,773] [jmetal.core.algorithm] [DEBUG] Creating initial set of solutions...\n",
+      "[2024-01-22 12:11:43,779] [jmetal.core.algorithm] [DEBUG] Evaluating solutions...\n",
+      "[2024-01-22 12:11:43,784] [jmetal.core.algorithm] [DEBUG] Initializing progress...\n",
+      "[2024-01-22 12:11:43,788] [jmetal.core.algorithm] [DEBUG] Running main loop until termination criteria is met\n",
+      "[2024-01-22 12:11:49,456] [jmetal.core.algorithm] [DEBUG] Finished!\n"
+     ]
+    }
+   ],
+   "source": [
+    "algorithm.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "78bb9892",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "front = get_non_dominated_solutions(algorithm.get_result())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ed4066c2-8f24-41e0-95d8-7ca534b2be67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "92332a74-a845-4448-a8ea-79743ef7787b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "objectives = [solution.objectives for solution in front]\n",
+    "\n",
+    "x = [_[0] for _ in objectives]\n",
+    "y = [_[1] for _ in objectives]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "0e7af84a-5604-4d17-82b7-ddc0e8862bb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1378b5a30>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x, y, color=\"black\")\n",
+    "\n",
+    "plt.xlabel(\"f1\")\n",
+    "plt.ylabel(\"f2\")\n",
+    "plt.title(problem.name())\n",
+    "plt.legend([\"NSGAII\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7b871f7-170c-4ab9-8223-a018f02929c2",
+   "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "78bb9892",
+   "id": "d3eb1e3b-bb1f-42bf-b216-e9693099861d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fdca13ae-d8ca-47a0-9dd4-7a4e8c271046",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -195,7 +284,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,