Genetic Algorithm, Concepts and Working

person icon Dr. DEEBA KANNAN

Genetic Algorithm, Concepts and Working

Genetic Algorithm

updated on icon Updated on Sep, 2023

language icon Language - English

person icon Dr. DEEBA KANNAN

architecture icon Computer Science,Artificial Intelligence,IT & Software


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Course Description

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

These steps indicated that Genetic Algorithm is a Randomized, search-based optimization Algorithm.

This course is divided into four modules.

  • First module – Introduction, history, and terminologies used in Genetic Algorithms.
  • 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!!!


What will you learn in this course:

  • Evolutionary Computation and Genetic Algorithms

  • Terminologies and operators of Genetic Algorithm

  • Advanced Operators and Techniques in Genetic Algorithm

  • Simple Python code for Genetic Algorithm implementation

  • Applications of Genetic Algorithm


What are the prerequisites for this course?

  • No prerequisites are there for this course. Students can listen to the lectures to understand Genetic Algorithm concepts from the base.

Genetic Algorithm, Concepts and Working


Check out the detailed breakdown of what’s inside the course

History of Genetic Algorithm
4 Lectures
  • play icon Introduction to the course on Genetic Algorithm 04:11 04:11
  • play icon History of Genetic Algorithm 08:04 08:04
  • play icon Terminologies in Genetic Algorithms 09:33 09:33
  • play icon Introduction to Genetic Algorithm
Working of Genetic Algorithm
3 Lectures
Elements of Genetic Algorithm
5 Lectures
Applications of Genetic Algorithms
4 Lectures

Instructor Details



Deeba Kannan

Hi!! Im Deeba Kannan, working Assistant Professor in SRM Institute of Science and Technology. I completed my Doctorate in the field of IoT and Deep learning  model for Agricultural Enhancement. I love teaching and I am teaching B-Tech students for past 10 years. I do few youtube video lectures for my students in Theory of Computation, Artificial Intelligence and Compiler Design. Im interested more in to the core and base of computer science subjects. 

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