Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788472173
Length 384 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
 Deshpande Deshpande
Author Profile Icon Deshpande
Deshpande
 Kumar Kumar
Author Profile Icon Kumar
Kumar
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
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 1. Other Books You May Enjoy Index

Summary


In this chapter, we have introduced the concept of genetic algorithms (GAs) and programming constructs related to GAs. These algorithms derive inspiration from the natural process of evolution. Living species evolve by inheritance, variation in partner selection, and hence attributes of the offspring and occasional (random) mutation in the genetic code (DNA structure). The same concepts are applied in the GAs in order to search the best possible solution from a vast space of possible options. The algorithm is best applied to problems where brute force is insufficient and cannot reach a solution within a reasonable time.

We have seen the structure of GAs in general and implemented a solution for a simple problem in Java. We have reviewed some of the features of the KEEL framework and how it is very easy to translate data into knowledge. KEEL is a Java-based desktop application that facilitates the analysis of the behavior of evolutionary learning in different areas of learning and...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime
Visually different images