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Genetic Algorithm for Machine Learning
Rating: 4.4 out of 5(63 ratings)
3,650 students

Genetic Algorithm for Machine Learning

Simplified Way to Learn
Created byParteek Bhatia
Last updated 1/2021
English

What you'll learn

  • 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
  • Mutation
  • Concept of Probability of Mutation and Its usage in generation of new features
  • Concept and Implementation of Elitism

Course content

1 section • 8 lectures • 1h 32m total length

  • Concept of Genetic Algorithm13:44

    Genetic algorithm for machine learning explores natural selection, crossover, and mutation to optimize solutions and select global minima from a population of candidates.

  • Key terms used in GA & Its Mathematical Representation10:02

    Explore the mathematical representation of genetic algorithms, covering population, chromosome, gene, fitness function, objective, search space, variables, constraints, and genetic representations.

  • Implementation of Natural Selection: Part-19:40

    Illustrates natural selection in genetic algorithms using the roulette wheel algorithm, assigning probabilities by fitness to pick the most fitted individuals for the next generation.

  • Implementation of Natural Selection: Part-211:08

    Explore implementing natural selection in genetic algorithms using roulette wheel selection, including normalizing fitness, computing cumulative values, and selecting parents with a random number.

  • Implementation of Recombination: Part-19:08

    Explore recombination in genetic algorithms by exchanging genes between selected parents to create the next generation, including single point, stochastic single point, and double blind crossover.

  • Implementation of Recombination: Part-212:56

    Explore the implementation of recombination in genetic algorithms by using probability of crossover to decide whether parents or children advance to the next generation, via single or double point crossover.

  • Implementation of Mutation10:55

    Explore how mutation introduces new features in genetic algorithms by randomly changing genes during recombination. Learn how to set and apply a per-gene mutation probability to generate diverse populations.

  • Concept and Implementation of Elitism14:33

    Explore elitism in genetic algorithms, which preserves the best elites for the next generation by bypassing crossover and mutation and is guided by an elitism ratio.

Requirements

  • No

Description

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

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