Description

This tutorial covers the topic of Genetic Algorithms. From this tutorial, you will be able to understand the basic concepts and terminology involved in Genetic Algorithms. We will also discuss the various crossover and mutation operators, survivor selection, and other components as well.

Also, there will be other advanced topics that deal with topics like Schema Theorem, GAs in Machine Learning, etc. which are also covered in this tutorial.

After going through this tutorial, the reader is expected to gain sufficient knowledge to come up with his/her own genetic algorithms for a given problem.

This tutorial is prepared for the students and researchers at the undergraduate/graduate level who wish to get “good solutions” for optimization problems “fast enough” which cannot be solved using the traditional algorithmic approaches.

Genetic Algorithms is an advanced topic. Even though the content has been prepared keeping in mind the requirements of a beginner, the reader should be familiar with the fundamentals of Programming and Basic Algorithms before starting with this tutorial.

Course Content

Genetic Algorithms Tutorial
Genetic Algorithms – Home
Genetic Algorithms Introduction
Genetic Algorithms Fundamentals
Genotype Representation
Genetic Algorithms Population
Genetic Algorithms Fitness Function
Genetic Algorithms Parent Selection
Genetic Algorithms Crossover
Genetic Algorithms Mutation
Survivor Selection
Termination Condition
Models Of Lifetime Adaptation
Effective Implementation
Advanced Topics
Application Areas
Further Readings
Genetic Algorithms Quick Guide
Genetic Algorithms Resources
Genetic Algorithms Discussion

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