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xi | |
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xv | |
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xvii | |
| Preface |
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xix | |
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I. Evolvable Hardware & Genetic Programming |
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1 | (108) |
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Routine High-Return Human-Competitive Evolvable Hardware |
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3 | (30) |
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4 | (1) |
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Automatic Circuit Synthesis |
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4 | (6) |
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High-Return Human-Competitive Intelligence |
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10 | (4) |
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Progression of More Substantial Results |
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14 | (4) |
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Description of Six 21st-Century Patented Circuits |
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18 | (2) |
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Results for Six 21st-Century Patented Circuits |
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20 | (4) |
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Commercial Practicality of GP |
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24 | (4) |
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28 | (5) |
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29 | (4) |
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Using Generative Representations to Evolve Robots |
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33 | (26) |
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33 | (1) |
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Generative Representations |
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34 | (3) |
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Generative Robots Encoding |
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37 | (6) |
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Oscillator Controlled Genobots |
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37 | (2) |
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39 | (1) |
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Neural-Network Controlled Genobots |
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40 | (2) |
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Generative Representation Example for Neural-Network Controlled Genobots |
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42 | (1) |
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43 | (1) |
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Evolutionary Design System |
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43 | (1) |
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Evolution of Oscillator Controlled Robots |
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44 | (4) |
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Neural-Network Controlled Robots |
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48 | (4) |
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Advantages of a Generative Representation |
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52 | (3) |
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55 | (4) |
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57 | (2) |
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Intrinsic Evolutionary Design of Analog Building Blocks for Fuzzy Logic Controllers |
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59 | (22) |
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60 | (2) |
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Analog Fuzzy Logic Controllers |
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62 | (2) |
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Evolvable Hardware Platform |
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64 | (6) |
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Analog Reconfigurable Circuit |
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67 | (2) |
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Evolution of a Fuzzy Circuit |
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69 | (1) |
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70 | (4) |
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70 | (2) |
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72 | (1) |
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72 | (1) |
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73 | (1) |
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74 | (7) |
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77 | (4) |
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Improving the Search by Encoding Multiple Solutions in a Chromosome |
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81 | (28) |
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81 | (2) |
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Multiple Solution Programming |
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83 | (1) |
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Test Problems and Metric of Performance |
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83 | (1) |
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Multi Expression Programming |
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84 | (6) |
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84 | (1) |
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Decoding MEP Chromosomes and Fitness Assignment Process |
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85 | (1) |
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86 | (1) |
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87 | (1) |
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Single Expression Programming |
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87 | (1) |
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Numerical Experiments with MEP and SEP |
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88 | (2) |
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Linear Genetic Programming |
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90 | (6) |
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90 | (1) |
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90 | (1) |
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90 | (2) |
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92 | (1) |
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Multi Solution Linear Genetic Programming |
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93 | (1) |
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Numerical Experiments with LGP and MS-LGP |
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94 | (2) |
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Infix Form Genetic Programming |
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96 | (6) |
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96 | (1) |
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IFGP Individual Representation |
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96 | (2) |
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98 | (1) |
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Fitness Assignment Process |
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99 | (1) |
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100 | (1) |
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100 | (1) |
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Single Solution Infix Form Genetic Programming |
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101 | (1) |
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Numerical Experiments with IFGP and SS-IFGP |
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101 | (1) |
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102 | (7) |
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105 | (4) |
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109 | (108) |
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Real-World Evolutionary Designs: Secure Evolvable Hardware for Public-Key Cryptosystems |
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111 | (28) |
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112 | (1) |
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113 | (1) |
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Evolving Hardware for Digital Circuits |
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114 | (6) |
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Circuit Specification Encoding |
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114 | (2) |
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Circuit Specification Reproduction |
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116 | (2) |
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Circuit Specification Evaluation |
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118 | (2) |
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Evolutionary vs. Conventional Designs |
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120 | (8) |
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128 | (2) |
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Evolutionary vs. Conventional Hardware |
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130 | (5) |
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135 | (4) |
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137 | (2) |
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Automated Discovery of Innovative Designs of Mechanical Components Using Evolutionary Multi-objective Algorithms |
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139 | (26) |
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139 | (1) |
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Evolutionary Multi-objective Optimization (EMO) |
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140 | (1) |
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An Evolutionary Multi-objective Optimizer |
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141 | (2) |
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Constraint-Handling in NSGA-II |
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143 | (1) |
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EMO for Discovering Useful Design Variants |
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143 | (3) |
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Phase I: Finding a Set of Pareto-Optimal Solutions |
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144 | (1) |
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Phase II: Analyzing Optimized Solutions for Useful Properties |
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145 | (1) |
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Proposed Multi-objective Optimization Procedure |
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146 | (1) |
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Iterative local-search based EMO |
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147 | (1) |
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147 | (14) |
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148 | (4) |
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Comparison with NSGA-II Alone |
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152 | (2) |
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154 | (3) |
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157 | (2) |
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159 | (2) |
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161 | (4) |
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163 | (2) |
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Toward Efficient Topological Synthesis of Dynamic Systems Using Genetic Programming |
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165 | (28) |
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166 | (1) |
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Basic Framework for Bond Graph Synthesis |
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167 | (6) |
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167 | (2) |
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Automated Synthesis of Bond Graphs by Genetic Programming |
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169 | (4) |
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173 | (4) |
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Node-Encoding for Bond Graph Synthesis by Genetic Programming |
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173 | (2) |
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Hybrid-Encoding for Bond Graph Synthesis by Genetic Programming |
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175 | (2) |
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Encoding with a Realizable Function Set |
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177 | (1) |
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Scalable Benchmark for Automated Synthesis |
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177 | (3) |
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Topologically Open-Ended Bond Graph Synthesis |
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180 | (6) |
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Search Bias of Representation in Topologically Open-Ended Synthesis |
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181 | (2) |
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Representation and Scalability: How Function Set Affects Efficiency |
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183 | (2) |
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Population Seeding and the Efficiency of Topology Search |
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185 | (1) |
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186 | (1) |
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187 | (6) |
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189 | (4) |
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The Role of Simulated Evolution in Bioinformatics |
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193 | (24) |
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193 | (2) |
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The Precursor of Bioinformatics |
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193 | (1) |
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194 | (1) |
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194 | (1) |
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An Overview of Bioinformatics |
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195 | (6) |
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Important Concepts in Bioinformatics |
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195 | (2) |
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197 | (1) |
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Multiple Sequence Alignments |
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198 | (1) |
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Functional Site Identifications |
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199 | (1) |
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Evolutionary Computing Applications in Bioinformatics |
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200 | (1) |
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Genome Reversal Distance Estimation |
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201 | (5) |
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201 | (1) |
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202 | (2) |
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The Evolutionary Computing Solution |
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204 | (1) |
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205 | (1) |
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Subset Problem for Phylogenetic Data |
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206 | (6) |
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206 | (1) |
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207 | (1) |
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208 | (1) |
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The Evolutionary Computing Solution |
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209 | (2) |
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211 | (1) |
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212 | (5) |
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213 | (4) |
| Index |
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217 | (2) |
| Reviewer List |
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219 | |
