Genetic Algorithm Concepts And Working

Genetic Algorithm Concepts and Working

What you'll learn
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
Requirements
No prerequisites are there for this course. Students can listen to the lectures to understand Genetic Algorithm concepts from base.
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 population2. Calculate the fitness function3. Choose the fittest individuals as parental chromosomes4. Perform crossover (Recombination)5. Perform Mutation6. Repeat this process until the termination conditionThis 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 exampleThird Module – Types of Encoding, Selection, Crossover and Mutation methodsFourth module – Coding and Applications of Genetic AlgorithmHappy Learning!!!

Overview

Section 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

Section 2: Working of Genetic Algorithm

Lecture 4 Flow of Working - Genetic Algorithm

Lecture 5 Example - Working of Genetic Algorithm

Section 3: Elements of Genetic Algorithm

Lecture 6 Types of Encoding

Lecture 7 Types of Selection

Lecture 8 Types of Crossover

Lecture 9 Types of Mutation

Section 4: Applications of GA

Lecture 10 Python Implementation of Genetic Algorithm

Lecture 11 Travelling Salesman Problem

Lecture 12 Neural Network Weight adjustment

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

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