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Model Building, Model Testing and Model Fitting |
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1 | (31) |
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Uses of Genetic Algorithms |
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1 | (2) |
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Optimizing or Improving the Performance of Operations Systems |
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1 | (1) |
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Testing and Fitting Quantitative Models |
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2 | (1) |
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Maximizing vs. Minimizing |
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2 | (1) |
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2 | (1) |
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3 | (3) |
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3 | (1) |
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Revising the Model for Revising the Data? |
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3 | (1) |
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Hierarchic or Stepwise Model Building: The Role of Theory |
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4 | (1) |
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Significance and Meaningfulness |
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4 | (2) |
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6 | (1) |
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An Example: Linear Regression |
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6 | (1) |
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Iterative Hill-Climbing Techniques |
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7 | (7) |
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Iterative Incremental Stepping Method |
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8 | (1) |
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An Example: Fitting the Continents Together |
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9 | (2) |
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Other Hill-Climbing Methods |
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11 | (1) |
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The Danger of Entrapment on Local Optima and Saddle Points |
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12 | (1) |
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The Application of Genetic Algorithms to Model Fitting |
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13 | (1) |
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Assay Continuity in a Gold Prospect |
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14 | (14) |
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Description of the Problem |
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14 | (1) |
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A Model of Data Continuity |
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15 | (3) |
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Fitting the Data to the Model |
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18 | (1) |
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The Appropriate Misfit Function |
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19 | (2) |
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Fitting Models of One or Two Parameters |
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21 | (4) |
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Fitting the Non-homogeneous Model 3 |
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25 | (3) |
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28 | (3) |
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29 | (2) |
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Compact Fuzzy Models and Classifiers through Model Reduction and Evolutionary Optimization |
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31 | (30) |
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31 | (2) |
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33 | (4) |
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The Takagi-Sugeno Fuzzy Model |
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34 | (1) |
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Data-Driven Identification by Clustering |
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35 | (2) |
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Estimating the Consequent Parameters |
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37 | (1) |
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Transparency and Accuracy of Fuzzy Models |
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37 | (4) |
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38 | (1) |
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Genetic Multi-objective Optimization |
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39 | (2) |
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41 | (3) |
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Fuzzy Model Representation |
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41 | (1) |
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42 | (1) |
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42 | (1) |
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42 | (1) |
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43 | (1) |
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43 | (1) |
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44 | (2) |
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44 | (2) |
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46 | (1) |
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46 | (3) |
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49 | (7) |
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Iris Classification Problem |
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51 | (1) |
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Solutions in the literature |
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52 | (1) |
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52 | (4) |
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56 | (5) |
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56 | (5) |
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On the Application of Reorganization Operators for Solving a Language Recognition Problem |
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61 | (38) |
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61 | (2) |
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Performance across a New Problem Set |
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62 | (1) |
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62 | (1) |
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63 | (11) |
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64 | (3) |
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67 | (2) |
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69 | (4) |
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73 | (1) |
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74 | (9) |
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75 | (3) |
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Specific Considerations for the Language Recognition Problem |
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78 | (5) |
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Data Obtained from the Experimentation |
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83 | (4) |
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General Evaluation Criteria |
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87 | (1) |
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88 | (3) |
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88 | (1) |
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89 | (2) |
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91 | (1) |
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Conclusions and Further Directions |
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91 | (8) |
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93 | (6) |
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Using GA to Optimise the Selection and Scheduling of Road Projects |
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99 | (36) |
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99 | (1) |
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Formulation of the Genetic Algorithm |
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100 | (6) |
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100 | (1) |
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The Elements of the Project Schedule |
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100 | (1) |
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100 | (6) |
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Mapping the GA String into a Project Schedule and Computing the Fitness |
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106 | (11) |
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107 | (1) |
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107 | (2) |
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Calculation of Project Benefits |
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109 | (5) |
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Calculating Trip Generation, Route Choice and Link Loads |
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114 | (3) |
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117 | (15) |
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Convergence of Solutions to the Problem |
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117 | (2) |
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119 | (3) |
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Similarity and Dissimilarity of Solutions: Euclidean Distance |
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122 | (10) |
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Conclusions: Scheduling Interactive Road Projects by GA |
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132 | (3) |
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Dissimilar Construction Schedules with High and Almost Equal Payoffs |
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133 | (1) |
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Similar Construction Schedules with Dissimilar Payoffs |
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133 | (1) |
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133 | (2) |
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Decoupled Optimization of Power Electronics Circuits Using Genetic Algorithms |
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135 | (32) |
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135 | (2) |
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Decoupled Regulator Configuration |
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137 | (3) |
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Optimization Mechanism of GA |
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139 | (1) |
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Chromosome and Population Structures |
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139 | (1) |
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140 | (1) |
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Fitness Functions for PCS |
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140 | (4) |
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141 | (2) |
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143 | (1) |
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144 | (1) |
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144 | (1) |
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144 | (4) |
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145 | (1) |
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OF6 and OF8 for Objective (2) and Objective (4) |
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145 | (3) |
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148 | (1) |
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148 | (3) |
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151 | (14) |
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165 | (2) |
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165 | (2) |
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Feature Selection and Classification in the Diagnosis of Cervical Cancer |
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167 | (36) |
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167 | (2) |
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169 | (1) |
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Feature Selection by Genetic Algorithm |
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170 | (4) |
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171 | (1) |
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172 | (1) |
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GA Feature Selection Performance |
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172 | (1) |
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173 | (1) |
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Developing a Neural Genetic Classifier |
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174 | (4) |
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174 | (1) |
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175 | (2) |
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177 | (1) |
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177 | (1) |
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178 | (1) |
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Validation of the Algorithm |
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178 | (8) |
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178 | (1) |
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Experiments on Two-Dimensional Data |
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179 | (1) |
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Results of Two-Dimensional Data Experiments |
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180 | (4) |
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Lessons from Artificial Data |
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184 | (1) |
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Experiments on a Cell Image Dataset |
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184 | (2) |
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Parameterization of the GA |
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186 | (3) |
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Parameterization Experiments |
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186 | (1) |
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Results of Parameterization Experiments |
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187 | (1) |
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Selecting the Neural Network Architecture |
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188 | (1) |
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Experiments with the Cell Image Dataset |
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189 | (14) |
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Slide-Based vs. Cell-Based Features |
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189 | (5) |
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Comparison with the Standard Approach |
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194 | (4) |
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198 | (1) |
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199 | (4) |
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Algorithms for Multidimensional Scaling |
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203 | (32) |
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203 | (6) |
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203 | (1) |
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What is Multidimensional Scaling? |
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204 | (4) |
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Standard Multidimensional Scaling Techniques |
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208 | (1) |
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Multidimensional Scaling Examined in More Detail |
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209 | (6) |
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A Simple One-Dimensional Example |
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209 | (2) |
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211 | (2) |
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Using Standard Multidimensional Scaling Methods |
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213 | (2) |
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A Genetic Algorithm for Multidimensional Scaling |
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215 | (6) |
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Random Mutation Operators |
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216 | (2) |
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218 | (1) |
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219 | (1) |
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Design and Use of a Genetic Algorithm for Multidimensional Scaling |
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219 | (2) |
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221 | (4) |
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221 | (1) |
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Using the Genetic Algorithm |
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222 | (1) |
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223 | (2) |
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225 | (7) |
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225 | (1) |
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Definition of Parameters and Variables |
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226 | (1) |
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227 | (1) |
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228 | (3) |
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Adapting the Program for C or C++ |
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231 | (1) |
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Using the Extended Program |
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232 | (3) |
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233 | (2) |
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Genetic Algorithm-Based Approach for Transportation Optimization Problems |
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235 | (40) |
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GA-Based Solution Approach for Transport Models |
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236 | (15) |
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236 | (1) |
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GAB Approach for Single-Objective Bilevel Programming Models |
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236 | (8) |
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GAB Approach for Multi-Objective Bilevel Programming Models |
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244 | (6) |
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250 | (1) |
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GAB Calibration Approach for Transport Models |
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251 | (16) |
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251 | (1) |
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251 | (2) |
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253 | (3) |
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GAB Calibration Procedure |
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256 | (1) |
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257 | (1) |
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258 | (9) |
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267 | (1) |
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267 | (8) |
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268 | (3) |
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271 | (4) |
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Solving Job-Shop Scheduling Problems by Means of Genetic Algorithms |
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275 | (20) |
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275 | (1) |
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The Job-Shop Scheduling Constraint Satisfaction Problem |
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276 | (1) |
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277 | (2) |
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279 | (3) |
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Variable and Value Ordering Heuristics |
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280 | (2) |
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Heuristic Initial Population |
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282 | (2) |
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284 | (7) |
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291 | (4) |
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292 | (3) |
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Applying the Implicit Redundant Representation Genetic Algorithm in an Unstructured Problem Domain |
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295 | (46) |
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295 | (1) |
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Motivation for Frame Synthesis Research |
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296 | (1) |
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Modeling the Conceptual Design Process |
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296 | (1) |
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Research in Frame Optimization |
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297 | (1) |
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The Implicit Redundant Representation Genetic Algorithm |
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297 | (2) |
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Implementation of the IRR GA Algorithm |
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299 | (1) |
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Suitabilty of the IRR GA in Conceptual Design |
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299 | (1) |
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The IRR Genotype/Phenotype Representation |
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299 | (4) |
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Provision of Dynamic Redundancy |
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301 | (1) |
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Controlling the Level of Redundancy in the IRR GA Initial Population |
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302 | (1) |
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Applying the IRR GA to Frame Design Synthesis in an Unstructured Domain |
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303 | (20) |
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Unstructured Design Problem Formulation |
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303 | (1) |
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IRR GA Genotype/Phenotype Representation for Frame Design Synthesis |
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304 | (7) |
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Use of Repair Strategies on Frame Design Alternatives |
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311 | (6) |
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Generation of Horizontal Members in Design Synthesis Alternatives |
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317 | (2) |
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Specification of Loads on Unstructured Frame design Alternatives |
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319 | (4) |
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Finite-Element Analysis of Frame Structures |
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323 | (1) |
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Deletion of Dynamically Allocated Nodal Linked Lists |
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323 | (1) |
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IRR GA Fitness Evaluation of Frame Design Synthesis Alternatives |
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323 | (8) |
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Statement of Frame Design Objectives Used as Fitness Functions |
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323 | (2) |
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Application of Penalty Terms in IRR GA Fitness Evaluation |
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325 | (6) |
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Discussion of the Genetic Control Operators Used by the IRR GA |
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331 | (3) |
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Fitness Sharing among Individuals in the Population |
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331 | (1) |
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Tournament Selection of New Population Individuals |
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332 | (1) |
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Multiple Point Crossover of Binary Strings |
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333 | (1) |
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Single-Bit Mutation of Binary Strings |
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334 | (1) |
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Results of the Implicit Redundant Representation Frame Synthesis Trials |
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334 | (5) |
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Evolved Design Solutions for the Frame Synthesis Unstructured Domain |
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335 | (1) |
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Synthesis versus Optimization of Frame Design Solutions Using IRR GA |
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335 | (4) |
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339 | (2) |
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339 | (2) |
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How to Handle Constraints with Evolutionary Algorithms |
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341 | (22) |
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342 | (1) |
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Constraint Handling in EAs |
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342 | (3) |
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345 | (6) |
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Heuristic Genetic Operators |
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345 | (1) |
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Knowledge-Based Fitness and Genetic Operators |
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346 | (1) |
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347 | (1) |
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348 | (1) |
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349 | (1) |
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Heuristic-Based Microgenetic Method |
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350 | (1) |
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Stepwise Adaptation to Weights |
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350 | (1) |
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351 | (1) |
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Assessment of EAs for CSPs |
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352 | (3) |
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355 | (8) |
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356 | (7) |
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An Optimized Fuzzy Logic Controller for Active Power Factor Corrector Using Genetic Algorithm |
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363 | (28) |
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363 | (2) |
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FLC for the Boost Rectifier |
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365 | (6) |
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Switching Rule for the Switch SW |
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366 | (1) |
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Fuzzy Logic Controller (FLC) |
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367 | (3) |
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370 | (1) |
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Optimization of FLC by the Genetic Algorithm |
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371 | (8) |
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Structure of the Chromosome |
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371 | (1) |
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371 | (4) |
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Formulation of Multi-objective Fitness Function |
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375 | (1) |
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376 | (1) |
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Crossover and Mutation Operations |
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376 | (2) |
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Validation of SI: Recovery of Valid Fuzzy Subsets |
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378 | (1) |
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379 | (10) |
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389 | (2) |
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389 | (2) |
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Multilevel Fuzzy Process Control Optimized by Genetic Algorithm |
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391 | (52) |
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391 | (1) |
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392 | (1) |
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393 | (6) |
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393 | (3) |
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Process Stability during Genetic Algorithm Optimizing |
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396 | (1) |
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397 | (2) |
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Opitimizing Aided by Genetic Algorithm |
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399 | (2) |
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Genetic Algorithm Parameter |
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399 | (2) |
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Laboratory Cascaded Plant |
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401 | (11) |
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Multilevel Control Using Genetic Algorithm |
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412 | (7) |
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Non-coordinated Multilevel Control Using a PID Controller |
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412 | (7) |
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Fuzzy Multilevel Coordinated Control |
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419 | (15) |
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421 | (13) |
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434 | (9) |
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436 | (7) |
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Evolving Neural Networks for Cancer Radiotherapy |
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443 | (41) |
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Introduction and Chapter Overview |
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443 | (1) |
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An Introduction to Radiotherapy |
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444 | (11) |
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Radiation Therapy Treatment Planning (RTP) |
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444 | (1) |
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445 | (1) |
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446 | (1) |
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Recent Developments and Areas of Active Research |
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447 | (4) |
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451 | (4) |
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Evolutionary Artificial Neural Networks |
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455 | (8) |
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456 | (2) |
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Evolving Network Architectures |
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458 | (2) |
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460 | (1) |
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461 | (1) |
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Addition of Virtual Samples |
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462 | (1) |
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463 | (1) |
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Radiotherapy Treatment Planning with EANNs |
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463 | (17) |
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The Backpropogation ANN for Treatment Planning |
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463 | (3) |
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466 | (5) |
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471 | (6) |
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Breast Cancer Treatment Planning |
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477 | (3) |
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480 | (2) |
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Discussion and Future Work |
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482 | (2) |
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484 | (1) |
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485 | (4) |
Index |
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489 | |