Genetic Algorithm for Machine Learning

Working Principle of Genetics Algorithms

Natural Selection

Implementation of Natural Selection through Roulette Wheel

Crossover or Recombination

Concept of Probability of Crossover and Its usage in generation of Population


Concept of Probability of Mutation and Its usage in generation of new features

Concept and Implementation of Elitism


  • No


This course covers the working Principle of Genetics Algorithms and its various components like Natural Selection, Crossover or Recombination, Mutation and Elitism in a a very simplified way.

GA are inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.

Who this course is for:

  • Students taking Genetics Algorithm or Machine Learning or Artificial Intelligence Course
  • Machine Learning Enthusiast
  • Students preparing for placement tests and interviews

Course content