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Genetic Algorithm Concepts and Working

Genetic Algorithm Concepts and Working

318 Students

3h5min

Beginner

4.5

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  • Overview
  • Curriculum
  • Discussion
  • Review
  • Instructor

What you will learn

  • Explore the principles of Evolutionary Computation and delve into Genetic Algorithms.

  • Familiarize with the key terminologies and operators essential for Genetic Algorithm operation.

  • Advance understanding through the exploration of sophisticated operators and techniques within Genetic Algorithms.

  • Acquire practical skills by implementing Genetic Algorithms through simple Python code.

  • Discover real-world applications where Genetic Algorithms offer effective solutions.

What are the requirements for taking your course

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

Who is this course for

  • Computer science students

  • Students doing research in Genetic Algorithm

  • Students interested in understanding the basic working of Genetic Algorithm

  • Interested in Nature inspired computing

  • Planning to Explore Evolutionary Computing

  • Planning to Explore Optimization Techniques

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

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!!!

History and Inspiration of Genetic Algorithm

Introduction to the course on Genetic Algorithm

History of Evolutionary Computing

Terminologies in Genetic Algorithms

Assessment 1 - Basic Terminologies

Working of Genetic Algorithm

Flow of Working - Genetic Algorithm

Example - Working of Genetic Algorithm

Python code

Assessment 2 - Working of Genetic Algorithm

Elements of Genetic Algorithm

Types of Encoding

Encoding Implementation

Types of Selection

Types of Crossover

Types of Mutation

Assessment 3 - Techniques involved

Applications of GA

Python Implementation of Genetic Algorithm

Travelling Salesman Problem

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Dr.Deeba K

Instructor

$15.76

This Course Includes

16 Lessons
4 Quizzes
0 Assignment
4 Downloadable Resources
English
Full Lifetime Access
Certificate of completion
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Genetic Algorithm Concepts and Working

318 students 3h5min