Genetic Algorithm Concepts and Working

Genetic Algorithm Concepts and Working, available at $69.99, has an average rating of 4.45, with 16 lectures, 4 quizzes, based on 68 reviews, and has 320 subscribers.

You will learn about 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. This course is ideal for individuals who are Computer science students or Students doing research in Genetic Algorithm or Students interested in understanding the basic working of Genetic Algorithm or Interested in Nature inspired computing or Planning to Explore Evolutionary Computing or Planning to Explore Optimization Techniques It is particularly useful for Computer science students or Students doing research in Genetic Algorithm or Students interested in understanding the basic working of Genetic Algorithm or Interested in Nature inspired computing or Planning to Explore Evolutionary Computing or Planning to Explore Optimization Techniques.

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Summary

Title: Genetic Algorithm Concepts and Working

Price: $69.99

Average Rating: 4.45

Number of Lectures: 16

Number of Quizzes: 4

Number of Published Lectures: 16

Number of Published Quizzes: 4

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: $19.99

Quality Status: approved

Status: Live

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.

Who Should Attend

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

Target Audiences

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

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!!!

Course Curriculum

Chapter 1: History and Inspiration of Genetic Algorithm

Lecture 1: Introduction to the course on Genetic Algorithm

Lecture 2: History of Evolutionary Computing

Lecture 3: Terminologies in Genetic Algorithms

Chapter 2: Working of Genetic Algorithm

Lecture 1: Flow of Working – Genetic Algorithm

Lecture 2: Example – Working of Genetic Algorithm

Lecture 3: Python code

Chapter 3: Elements of Genetic Algorithm

Lecture 1: Types of Encoding

Lecture 2: Encoding Implementation

Lecture 3: Types of Selection

Lecture 4: Types of Crossover

Lecture 5: Types of Mutation

Chapter 4: Applications of GA

Lecture 1: Python Implementation of Genetic Algorithm

Lecture 2: Travelling Salesman Problem

Lecture 3: Python Code for Genetic Algorithm

Lecture 4: Neural Network Weight adjustment

Lecture 5: Python code for Neural Networks using GA

Instructors

Dr.Deeba K
Assistant Professor in SRM IST, Kattankulathur, Chennai

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 2 votes
4 stars: 13 votes
5 stars: 52 votes

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