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BIF602 131 Genetic Algorithm operators (V-U)
- Course:Bioinformatics Computing II (V-U)
- Discipline:Basic and Health Sciences
- Institute:Virtual University
- Instructor(s): Dr. Muhammad Haroon Khan
- Level:Undergraduate
Bioinformatics Computing II (V-U)
- BIF602 001 Introduction to Natural Computing (V-U)
- BIF602 002 Introduction to Natural Computing (V-U)
- BIF602 003 Introduction to Natural Computing (V-U)
- BIF602 005 Introduction to Natural Computing (V-U)
- BIF602 004 Introduction to Natural Computing (V-U)
- BIF602 006 Introduction to Natural Computing (V-U)
- BIF602 007 Introduction to Natural Computing (V-U)
- BIF602 009 When to use natural computing approach (V-U)
- BIF602 008 Computing with natural materials (V-U)
- BIF602 010 Natural Phenomena, models and metap (V-U)
- BIF602 011 Natural Phenomena, models and metap (V-U)
- BIF602 012 From nature to computing and back a (V-U)
- BIF602 014 Parallelism and distributivity (V-U)
- BIF602 013 Individuals, entities and agents (V-U)
- BIF602 015 Interactivity (V-U)
- BIF602 017 Stigmergy (V-U)
- BIF602 016 Connectivity (V-U)
- BIF602 018 Adaptation (V-U)
- BIF602 019 Evolution-1 (V-U)
- BIF602 020 Evolution-II (V-U)
- BIF602 021 Feedback (V-U)
- BIF602 023 Negative feedback (V-U)
- BIF602 022 Positive Feedback (V-U)
- BIF602 024 Self-organization (V-U)
- BIF602 025 Characteristics of Self-organizatio (V-U)
- BIF602 026 Alternatives to Self-organization (V-U)
- BIF602 027 Complexity (V-U)
- BIF602 028 Emergence (V-U)
- BIF602 029 Reductionism (V-U)
- BIF602 030 Bottom-up vs top-down (V-U)
- BIF602 031 Determinism (V-U)
- BIF602 032 Chaos (V-U)
- BIF602 033 Fractals (V-U)
- BIF602 034 Introduction to physiological syste (V-U)
- BIF602 036 Comprehensive definition of physiol (V-U)
- BIF602 035 Comprehensive definition of physiol (V-U)
- BIF602 037 Input and the output of a system (V-U)
- BIF602 038 Historical Background of Modeling (V-U)
- BIF602 039 Historical Background of Modeling-I (V-U)
- BIF602 041 Evolution of Computer Power and Adv (V-U)
- BIF602 040 Modeling from Literature (V-U)
- BIF602 042 The Physiome Project (V-U)
- BIF602 043 The Physiome Project in detail (V-U)
- BIF602 044 Constructing The Physiome-I (V-U)
- BIF602 046 PREREQUISITES FOR THE PHYSIOME PROJ (V-U)
- BIF602 045 Constructing The Physiome-II (V-U)
- BIF602 047 WHY THE PHYSIOME-I (V-U)
- BIF602 048 WHY THE PHYSIOME-II (V-U)
- BIF602 049 Databases for the Physiome (V-U)
- BIF602 050 From molecules to humankind (V-U)
- BIF602 051 Strategies Toward Constructing Larg (V-U)
- BIF602 052 Strategies Toward Constructing Larg (V-U)
- BIF602 054 The Virtual Physiological Human (VP (V-U)
- BIF602 053 The Virtual Physiological Human (VP (V-U)
- BIF602 055 An example, a cardiome effort (V-U)
- BIF602 056 The cardiome effort (V-U)
- BIF602 058 Modeling from cellular to organ and (V-U)
- BIF602 057 Levels of modeling (V-U)
- BIF602 059 Classification of models (V-U)
- BIF602 060 Deterministic models (V-U)
- BIF602 061 Stochastic models (V-U)
- BIF602 063 Non-Parametric models (V-U)
- BIF602 062 Parametric models (V-U)
- BIF602 064 Applied Example-1 (V-U)
- BIF602 065 Applied Example-2 (V-U)
- BIF602 067 Detailed Compartmental Modeling (V-U)
- BIF602 066 Compartmental Modeling (V-U)
- BIF602 068 Modified Compartmental Models (V-U)
- BIF602 069 Expansion to Multi-Compartmental Mo (V-U)
- BIF602 071 Linear modeling of physiological co (V-U)
- BIF602 070 Applied example (V-U)
- BIF602 072 Applied Example-I (V-U)
- BIF602 073 Applied Example No. 2 (V-U)
- BIF602 075 The future of physiological systems (V-U)
- BIF602 074 Nonlinear modeling of physiological (V-U)
- BIF602 076 Professional societies and organiza (V-U)
- BIF602 077 Bio-inspired computation (V-U)
- BIF602 079 Information Organizes and Breeds Li (V-U)
- BIF602 078 What is Life? (V-U)
- BIF602 080 Emergence and Explanation (V-U)
- BIF602 081 Life and Information (V-U)
- BIF602 083 The Nature of Information and Infor (V-U)
- BIF602 082 The Logical Mechanisms of Life (V-U)
- BIF602 084 Formalizing Knowledge: Uncovering t (V-U)
- BIF602 085 Self-Organization and Emergent Comp (V-U)
- BIF602 087 Complex Self-organization (V-U)
- BIF602 086 Life on the Edge of Chaos? (V-U)
- BIF602 088 Evolutionary Computing (V-U)
- BIF602 089 Evolutionary Biology (V-U)
- BIF602 090 On the theory of Evolution (V-U)
- BIF602 092 Basic Principles of Genetics (V-U)
- BIF602 091 On the theory of Evolution-II (V-U)
- BIF602 093 Principles of Genetics in detail (V-U)
- BIF602 094 Pillars of Evolutionary Theory (V-U)
- BIF602 095 The genotype (V-U)
- BIF602 097 Cell Replication: Meoisis (V-U)
- BIF602 096 Cell Replication: Mitosis (V-U)
- BIF602 098 Genetic mutations (V-U)
- BIF602 100 Artificial Evolution-I (V-U)
- BIF602 099 A brief history of Evolutionary com (V-U)
- BIF602 102 Standard Evolutionary Algorithms (V-U)
- BIF602 101 Artificial Evolution-II (V-U)
- BIF602 103 Genetic encoding (V-U)
- BIF602 104 Binary representation (V-U)
- BIF602 105 Real-Valued representation (V-U)
- BIF602 107 Evolvability (V-U)
- BIF602 106 Tree Based representation (V-U)
- BIF602 108 Fitness Functions (V-U)
- BIF602 109 Population (V-U)
- BIF602 110 Selection Operators (V-U)
- BIF602 112 Genetic drift (V-U)
- BIF602 111 Selection Pressure (V-U)
- BIF602 113 Proportionate selection (V-U)
- BIF602 114 Roulette wheel selection (V-U)
- BIF602 115 Rank based selection (V-U)
- BIF602 117 Tournament selection (V-U)
- BIF602 116 Truncated Rank based selection (V-U)
- BIF602 118 Elitism (V-U)
- BIF602 119 Genetic operators (V-U)
- BIF602 121 Mutation (V-U)
- BIF602 120 Crossover (V-U)
- BIF602 122 Survivor selection (V-U)
- BIF602 123 Initialization and termination (V-U)
- BIF602 124 Evolutionary measures (V-U)
- BIF602 125 Evolutionary Algorithms (V-U)
- BIF602 126 Genetic Algorithms (V-U)
- BIF602 128 The goals of optimization (V-U)
- BIF602 127 Robustness of traditional search an (V-U)
- BIF602 129 Genetic Algorithm and traditional s (V-U)
- BIF602 130 Elements of Genetic Algorithms (V-U)
- BIF602 131 Genetic Algorithm operators (V-U)
- BIF602 132 A simple Genetic Algorithm-I (V-U)
- BIF602 133 A simple Genetic Algorithm-II (V-U)
- BIF602 135 Genetic Algorithm at work: A simula (V-U)
- BIF602 134 Genetic Algorithm at work: A simula (V-U)
- BIF602 136 Applications of Genetic Algorithms (V-U)
- BIF602 137 Genetic Programming (GP) (V-U)
- BIF602 139 Progress in Genetic Programming (V-U)
- BIF602 138 Genetic Programming Challenges (V-U)
- BIF602 140 Representation in Tree-based Geneti (V-U)
- BIF602 141 Initialising the Population for Gen (V-U)
- BIF602 142 Selection in Genetic Programming (V-U)
- BIF602 144 Getting Ready to Run Genetic Progra (V-U)
- BIF602 143 Recombination and Mutation in Genet (V-U)
- BIF602 145 Steps 1: Terminal Set (V-U)
- BIF602 146 Step 2: Function Set (V-U)
- BIF602 147 Step 3: Fitness Function (V-U)
- BIF602 149 Evolutionary Programming (V-U)
- BIF602 148 Steps 4 and 5: Parameters and Termi (V-U)
- BIF602 150 Evolutionary Programming operators (V-U)
- BIF602 152 Evolution Strategies-II (V-U)
- BIF602 151 Evolution Strategies-I (V-U)
- BIF602 153 Swarm Intellegence (V-U)
- BIF602 154 Ant colony optimization-I (V-U)
- BIF602 156 Evolution strtaegies II (V-U)
- BIF602 155 Ant colony optimization-II (V-U)
- BIF602 157 Swarm intelligence (V-U)
- BIF602 158 Ant colony optimization-I (V-U)
- BIF602 159 Ant colony optimization-II (V-U)
- BIF602 161 Particle swarm optimzation I (V-U)
- BIF602 160 Ant colony optimization example (V-U)
- BIF602 162 Particle swarm optimzation II (V-U)
- BIF602 163 Bees Algorithm (V-U)
- BIF602 165 Introduction to neural network (V-U)
- BIF602 164 Bees Algorithm Detail (V-U)
- BIF602 166 Biological neural network (V-U)
- BIF602 167 Biological neural network (V-U)
- BIF602 169 Neurode (V-U)
- BIF602 168 Artifical Neural Network (V-U)
- BIF602 170 Neural Network and Neural Network Based System (V-U)
- BIF602 171 Learning vs Training (V-U)
- BIF602 172 ANN Learning (V-U)
- BIF602 174 Neural Network Un-Supervised Learning Algorithm (V-U)
- BIF602 173 Neural Network Supervised Learning Algorithm (V-U)
- BIF602 175 Perceptron I (V-U)
- BIF602 176 Perceptron II (V-U)
- BIF602 177 Back Propagation I (V-U)
- BIF602 179 Hopfield Network I (V-U)
- BIF602 178 Back Propagation II (V-U)
- BIF602 180 Hopfield Network II (V-U)
- BIF602 181 Learning Vector Quantization (V-U)
- BIF602 182 Self Organzing Map I (V-U)
- BIF602 184 Advantages and Disadvantages of Artificial Neural Network (V-U)
- BIF602 183 Self Organzing Map II (V-U)
- BIF602 185 Neural Network Applications in Bioinformatics (V-U)
- BIF602 186 Neural Network Architectures in Protein Bioinformatics (V-U)
- BIF602 188 Prediction of Protein Secondary Structure with Neural Network (V-U)
- BIF602 187 Neural Network Architectures in Protein Bioinformatics (V-U)
- BIF602 189 Prediction of Binding Sites with Neural Network (V-U)
- BIF602 190 Prediction of Relative Solvent Accessibility (RSA) with Neural Network (V-U)
- BIF602 192 (V-U)
- BIF602 191 Coding region and recognition and gene identification (V-U)
- BIF602 193 (V-U)
- BIF602 194 (V-U)
- BIF602 196 (V-U)
- BIF602 195 (V-U)
- BIF602 197 Hardware Artificial Life (V-U)
- BIF602 198 (V-U)
- BIF602 199 Wetware Artificial Life (V-U)
- BIF602 200 autonomous agents (V-U)
- BIF602 201 Digital evolution (V-U)
- BIF602 202 (V-U)
- BIF602 204 AIS (V-U)
- BIF602 203 (V-U)
- BIF602 205 Biological Immune System (V-U)
- BIF602 206 Immune Network Theory (V-U)
- BIF602 207 Negative selection (V-U)
- BIF602 209 Intrusion detection systems (V-U)
- BIF602 208 Clonal Selection (V-U)
- BIF602 210 (V-U)
- BIF602 211 (V-U)
- BIF602 212 (V-U)
- BIF602 213 (V-U)
- BIF602 215 (V-U)
- BIF602 214 (V-U)
- BIF602 216 (V-U)
- BIF602 217 (V-U)
- BIF602 218 DNA Computing (V-U)
- BIF602 219 DNA Computing (V-U)
- BIF602 221 Basics of DNA (V-U)
- BIF602 220 DNA Computing (V-U)
- BIF602 222 DNA Computing (V-U)
- BIF602 223 DNA Computing (V-U)
- BIF602 224 DNA Computing (V-U)
- BIF602 225 (V-U)
- BIF602 226 efficiency of DNA Computing (V-U)
- BIF602 227 success of DNA Computing (V-U)
- BIF602 228 (V-U)
- BIF602 230 (V-U)
- BIF602 229 (V-U)
- BIF602 231 (V-U)
- BIF602 232 (V-U)
- BIF602 233 Caveats (V-U)
- BIF602 234 Application of DNA Computing (V-U)
- BIF602 236 Advantages of of DNA Computing (V-U)
- BIF602 235 (V-U)
- BIF602 237 (V-U)
- BIF602 238 String matching (V-U)
- BIF602 239 Aproximate String matching (V-U)
- BIF602 241 sequence aligment (V-U)
- BIF602 240 Dynamic programming (V-U)
- BIF602 242 pairwise sequence aligment (V-U)
- BIF602 243 global vs local alignment (V-U)
- BIF602 244 global alignment fundamentals (V-U)
- BIF602 246 Trace Back (V-U)
- BIF602 245 Matrix filling (V-U)
- BIF602 247 GSA (V-U)
- BIF602 248 Local Alignement (V-U)
- BIF602 249 Matrix filling in LSA (V-U)
- BIF602 250 Trace Back in LSA (V-U)
- BIF602 251 LSA (V-U)
- BIF602 252 Importance of Cost Functions (V-U)
- BIF602 253 Multiple Sequence Alignment (V-U)
- BIF602 255 scoring a Multiple Sequence Alignment (V-U)
- BIF602 254 Biological Motivation (V-U)
- BIF602 256 Dynamic Programming Algorithm (V-U)
- BIF602 257 Progressive Alignment Approaches (V-U)
- BIF602 258 Star Alignment (V-U)
- BIF602 260 Exercise star alignment (V-U)
- BIF602 259 Complexity analysis (V-U)
- BIF602 261 T-Coffee (V-U)
- BIF602 262 Decision Tree (V-U)
- BIF602 263 Nodes and Branches in Decision Tree (V-U)
- BIF602 265 Decision Tree (V-U)
- BIF602 264 Classification Trees (V-U)
- BIF602 266 Applications of Decision Trees to Computational Biology (V-U)