Genetic Algorithms Tutorial

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What is Genetic Algorithms?

This training covers the area of Genetic Algorithms. From this lecture, you will be able to recognize the basic concepts and language involved in Genetic Algorithms. We will also talk over the numerous crossover and mutation operators, survivor selection, and other mechanisms as well.
Similarly, there will be other advanced topics that deal with topics like Schema Theorem, GAs in Machine Learning, etc. which are also covered in this lecture.
Once successfully completing this tutorial, the person who reads is likely to gain enough knowledge to come up with his/her own genetic algorithms for a given problem.

Audience

This lecture is equipped for the students and scholars at the undergraduate/graduate level who hope to get “good solutions” for optimization problems “fast enough” which cannot be solved using the traditional algorithmic approaches.

Prerequisites

Genetic Algorithms is an innovative topic. Even though the content has been ready keeping in mind the necessities of a beginner, the reader should be aware with the basics of Programming and Basic Algorithms before starting with this lecture.

Genetic Algorithms Tutorial: List of Topics

• Genetic Algorithms
  • 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
  • Genetic Algorithms Survivor Selection
  • Genetic Algorithms Termination Condition
  • Models Of Lifetime Adaptation
  • Effective Implementation
  • Genetic Algorithms Advanced Topics
  • Genetic Algorithms Application Areas
  • Genetic Algorithms Further Readings