Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

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...