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Artificial Intelligence for Big Data

You're reading from   Artificial Intelligence for Big Data Complete guide to automating Big Data solutions using Artificial Intelligence techniques

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788472173
Length 384 pages
Edition 1st Edition
Languages
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Authors (2):
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Anand Deshpande Anand Deshpande
Author Profile Icon Anand Deshpande
Anand Deshpande
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Big Data and Artificial Intelligence Systems 2. Ontology for Big Data FREE CHAPTER 3. Learning from Big Data 4. Neural Network for Big Data 5. Deep Big Data Analytics 6. Natural Language Processing 7. Fuzzy Systems 8. Genetic Programming 9. Swarm Intelligence 10. Reinforcement Learning 11. Cyber Security 12. Cognitive Computing 13. Other Books You May Enjoy

Genetic algorithms structure


In this section, let's understand the structure of a genetic algorithm that finds the optimum solution for a problem where the search space is so huge that brute force cannot solve it. The core algorithm was proposed by John Holland in 1975. In general, Genetic Algorithm provides an ability to provide a good enough solution fast enough to be reasonable. The generic flow of a Genetic Algorithm is depicted in the diagram:

Let's try to illustrate Genetic Algorithm with a simple example. Consider that you have to find out a number (integer) in millions of values (the solution space). We can follow the steps in the algorithm and reach the target solution much quicker than application of a brute force method. Here is the implementation of the algorithm in Java:

  1. Define the GA class with a simple constructor to initialize the population:
  public GA(int solutionSpace, int populationSize,int targetValue, int maxGenerations, int mutationPercent) {

    this.solutionSpace...
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