Course Overview
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Course Synopsis
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This course has been designed to give an interdisciplinary perspective on computational problems in molecular biology. This course aims to addresses several algorithmic challenges in Bioinformatics. Students will learn various ways in which they can apply computing techniques to the problems of molecular life sciences. This area has been very hot in recent years and there is an extensive arsenal of computational methods available.
The course will familiarize the students with the recent techniques of computing. By the end of the course, the students will have a handsome knowledge of natural computing, neural networks and pattern recognition etc. for solving bioinformatics problems and a grasp of the underlying principles that is adequate for them to evaluate, use and develop novel techniques as needed.
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Course Learning Outcomes
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Computationl Problems in Biology
- Introduction to Computational Biology
- Natural Computing and its Empowerment and How nature behaves and works.
- Natural Computing processing and its attributes
- Networking and Neural Networking
- System Modeling
- Databases and Virtual Physiological human Initiatives
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Course Calendar
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Week 01
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1
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Introduction to Natural Computing
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2
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Introduction to Natural Computing Part-2
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3
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Introduction to Natural Computing Part-3 (A Small sample of ideas)
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4
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Introduction to Natural Computing Part-4 (The philosophy of natural computing)
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5
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Introduction to Natural Computing Part-5 (The Three branches: a brief overview)
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6
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Introduction to Natural Computing Part-6 (Computing inspired by nature)
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7
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Introduction to Natural Computing Part-7 (The simulation and emulation of nature in computers)
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8
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Computing with natural materials
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9
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When to use natural computing approaches
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Week 02
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10
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Natural Phenomena, models and metaphores-I
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11
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Natural Phenomena, models and metaphores-II
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12
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From nature to computing and back again
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13
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Individuals, entities and agents
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14
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Parallelism and distributivity
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Week 03
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25
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Characteristics of Self-organization
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26
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Alternatives to Self-organization
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Week 04
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34
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Introduction to physiological systems modeling
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35
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Comprehensive definition of physiological systems modeling, simulation, and control-I
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36
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Comprehensive definition of physiological systems modeling, simulation, and control-II
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37
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Input and the output of a system
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Week 05
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38
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Historical Background of Modeling
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39
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Historical Background of Modeling-II
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40
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Modeling from Literature
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41
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Evolution of Computer Power and Advancements in Physiological Systems Modeling
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43
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The Physiome Project in detail
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44
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Constructing The Physiome-I
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45
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Constructing The Physiome-II
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46
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PREREQUISITES FOR THE PHYSIOME PROJECT
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Week 06
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49
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Databases for the Physiome
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50
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From molecules to humankind
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51
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Strategies Toward Constructing Large Models-I
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52
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Strategies Toward Constructing Large Models-II
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53
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The Virtual Physiological Human (VPH) initiative
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54
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The Virtual Physiological Human (VPH)
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55
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An example, a cardiome effort
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Week 07
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58
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Modeling from cellular to organ and systems modeling
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59
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Classification of models
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66
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Compartmental Modeling
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Week 08
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67
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Detailed Compartmental Modeling
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68
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Modified Compartmental Models
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69
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Expansion to Multi-Compartmental Models
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71
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Linear modeling of physiological control systems
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74
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Nonlinear modeling of physiological control systems
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75
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The future of physiological systems modeling, simulation, and control
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76
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Professional societies and organizations
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Week 09
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77
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Bio-inspired computation
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79
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Information Organizes and Breeds Life
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80
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Emergence and Explanation
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82
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The Logical Mechanisms of Life
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83
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The Nature of Information and Information Processes in Nature
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84
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Formalizing Knowledge: Uncovering the Design Principles of Nature
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85
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Self-Organization and Emergent Complex Behavior
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86
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Life on the Edge of Chaos?
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87
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Complex Self-organization
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88
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Evolutionary Computing
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Week 10
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90
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On the theory of Evolution
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91
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On the theory of Evolution-II
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92
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Basic Principles of Genetics
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93
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Principles of Genetics in detail
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94
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Pillars of Evolutionary Theory
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96
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Cell Replication: Mitosis
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97
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Cell Replication: Meoisis
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99
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A brief history of Evolutionary computation (EC)
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Week 11
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100
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Artificial Evolution-I
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101
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Artificial Evolution-II
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102
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Standard Evolutionary Algorithms
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104
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Binary representation
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105
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Real-Valued representation
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106
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Tree Based representation
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Week 12
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113
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Proportionate selection
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114
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Roulette wheel selection
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116
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Truncated Rank based selection
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Week 13
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123
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Initialization and termination
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124
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Evolutionary measures
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125
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Evolutionary Algorithms
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127
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Robustness of traditional search and optimization methods
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Week 14
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128
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The goals of optimization
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129
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Genetic Algorithm and traditional search methods
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130
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Elements of Genetic Algorithms
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131
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Genetic Algorithm operators
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132
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A simple Genetic Algorithm-I
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133
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A simple Genetic Algorithm-II
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134
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Genetic Algorithm at work: A simulation by hand-I
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135
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Genetic Algorithm at work: A simulation by hand-II
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136
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Applications of Genetic Algorithms
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137
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Genetic Programming (GP)
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138
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Genetic Programming Challenges
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139
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Progress in Genetic Programming
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Week 15
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140
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Representation in Tree-based Genetic Programming
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141
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Initialising the Population for Genetic Programming
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142
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Selection in Genetic Programming
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143
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Recombination and Mutation in Genetic Programming
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144
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Getting Ready to Run Genetic Programming
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145
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Steps 1: Terminal Set
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147
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Step 3: Fitness Function
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148
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Steps 4 and 5: Parameters and Termination
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Week 16
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149
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Evolutionary Programming
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150
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Evolutionary Programming operators
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151
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Evolution Strategies-I
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152
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Evolution Strategies-II
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154
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Ant colony optimization-I
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155
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Ant colony optimization-II
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