Nature Inspired Optimization Algorithms
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Follow on Instagram for UpdatesNature Inspired Optimization Algorithms Syllabus — Unit-wise Content
The official GTU curriculum for Nature Inspired Optimization Algorithms (subject code 3174508) as prescribed for CS-IoT · Semester 7 divides the course into 6 units, covering 36 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3174508)Unit 1 INTRODUCTION
- From Nature to Nature Computing
- Three Branches: A Brief Overview
- Entities and agents - Parallelism and Distributivity Interactivity
- Emergence and Bottom-up Vs Top-Down- Determination
- Chaos and Fractals
Unit 2 COMPUTING INSPIRED BY NATURE
- Evolutionary Computing
- Hill Climbing and Simulated Annealing
- Darwin's Dangerous Idea
- Genetics Principles
- Standard Evolutionary Algorithm- Genetic Algorithms
- Evolutionary Programming
- Genetic Programming
Unit 3 SWARM INTELLIGENCE
- Introduction - Ant Colonies
- Ant Foraging Behavior
- Ant Colony Optimization
- SACO and scope of ACO algorithms
- Ant Colony Algorithm (ACA)
- Swarm Robotics
- Foraging for food
- Social Adaptation of Knowledge
- Particle Swarm Optimization (PSO)
- Subject Code : 3174508 Subject Name : Nature Inspired Optimization Algorithms
Unit 4 GENETIC ALGORITHM
- Representation – Reproduction – Crossover and Mutation Operators – Crossover and Mutation rates – Selection mechanisms – Fitness proportionate – ranking and tournament selection – Building Block – Hypothesis and Schema Theorem
Unit 5 IMMUNO COMPUTING
- Introduction- Immune System
- Physiology and main components
- Pattern Recognition and Binding
- Immune Network Theory- Danger Theory
- Evaluation Interaction Immune Algorithms
- Introduction – Genetic algorithms
- Bone Marrow Models
- Forest's Algorithm
- Artificial Immune Networks
Unit 6 COMBINATORIAL OPTIMIZATION
- Travelling Salesman Problem (TSP)
- Graph Coloring Problem
- Job Scheduling problem
- Network Routing Problem
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