- Overview
- Curriculum
- Discussion
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- Instructor
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Explore the principles of Evolutionary Computation and delve into Genetic Algorithms.
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Familiarize with the key terminologies and operators essential for Genetic Algorithm operation.
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Advance understanding through the exploration of sophisticated operators and techniques within Genetic Algorithms.
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Acquire practical skills by implementing Genetic Algorithms through simple Python code.
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Discover real-world applications where Genetic Algorithms offer effective solutions.
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No prerequisites are there for this course. Students can listen to the lectures to understand Genetic Algorithm concepts from base.
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Computer science students
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Students doing research in Genetic Algorithm
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Students interested in understanding the basic working of Genetic Algorithm
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Interested in Nature inspired computing
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Planning to Explore Evolutionary Computing
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Planning to Explore Optimization Techniques
Genetic Algorithm is a search based optimization algorithm used to solve problems were traditional methods fails. It is an randomized algorithm where each step follows randomization principle.
Genetic Algorithm was developed by John Holland, from the University of Michigan, in 1960. He proposed this algorithm based on the Charles Darwin’s theory on Evolution of organism. Genetic Algorithm follows the principal of “Survival of Fittest”. Only the fittest individual has the possibility to survive to the next generation and hence when the generations evolve only the fittest individuals survive.
Genetic Algorithms operates on Solutions, hence called as search based optimization algorithm. It search for an optimal solution from the existing set of solutions in search space. The process of Genetic Algorithm is given as,
1. Randomly choose some individuals (Solutions) from the existing population
2. Calculate the fitness function
3. Choose the fittest individuals as parental chromosomes
4. Perform crossover (Recombination)
5. Perform Mutation
6. Repeat this process until the termination condition
This steps indicated that Genetic Algorithm is an Randomized, search based optimization Algorithm.
This course is divided into four modules.
First module – Introduction, history and terminologies used in Genetic Algorithm.
Second Module – Working of genetic algorithm with an example
Third Module – Types of Encoding, Selection, Crossover and Mutation methods
Fourth module – Coding and Applications of Genetic Algorithm
Happy Learning!!!
Introduction to the course on Genetic Algorithm
Introduction to the course on Genetic Algorithm
History of Evolutionary Computing
History of Evolutionary Computing
Terminologies in Genetic Algorithms
Terminologies in Genetic Algorithms
Assessment 1 - Basic Terminologies
Assessment 1 - Basic Terminologies
Flow of Working - Genetic Algorithm
Flow of Working - Genetic Algorithm
Example - Working of Genetic Algorithm
Example - Working of Genetic Algorithm
Python code
Python code
Assessment 2 - Working of Genetic Algorithm
Assessment 2 - Working of Genetic Algorithm
Types of Encoding
Types of Encoding
Encoding Implementation
Encoding Implementation
Types of Selection
Types of Selection
Types of Crossover
Types of Crossover
Types of Mutation
Types of Mutation
Assessment 3 - Techniques involved
Assessment 3 - Techniques involved
Python Implementation of Genetic Algorithm
Python Implementation of Genetic Algorithm
Travelling Salesman Problem
Travelling Salesman Problem
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Dr.Deeba K
Instructor
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