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Scala for Machine Learning, Second Edition

You're reading from Scala for Machine Learning, Second Edition Build systems for data processing, machine learning, and deep learning

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787122383

Length 740 pages

Edition 2nd Edition

Languages

Scala

Tools

Apache Spark

Concepts

Data Processing

Author (1):

R. Nicolas

View More author details

Table of Contents (27) Chapters

Scala for Machine Learning Second Edition

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started

2. Data Pipelines FREE CHAPTER

3. Data Preprocessing

4. Unsupervised Learning

5. Dimension Reduction

6. Naïve Bayes Classifiers

7. Sequential Data Models

8. Monte Carlo Inference

9. Regression and Regularization

10. Multilayer Perceptron

11. Deep Learning

12. Kernel Models and SVM

13. Evolutionary Computing

14. Multiarmed Bandits

15. Reinforcement Learning

16. Parallelism in Scala and Akka

17. Apache Spark MLlib

Basic Concepts

References

Index

Chapter 13. Evolutionary Computing

There's a lot more to evolutionary computing than genetic algorithms. The first foray into evolutionary computing was motivated by the need to address different types of large combinatorial problems also known as NP problems. This field of research was pioneered by John Holland [10:1] and David Goldberg [10:2] to leverage the theory of evolution and biology to solve combinatorial problems. Their findings should be of interest to anyone eager to learn about the foundation of genetic algorithms (GA) and artificial life.

This chapter covers the following topics:

The origin of evolutionary computing
The purpose and foundation of genetic algorithms as well as their benefits and limitations

From a practical perspective, you will learn how to:

Apply genetic algorithms to leverage a technical analysis of market price and volume movement to predict future returns
Evaluate or estimate the search space
Encode solutions in the binary format using either hierarchical or flat...

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Authors (1)

R. Nicolas

R. Nicolas

Patrick R. Nicolas is the director of engineering at Agile SDE, California. He has more than 25 years of experience in software engineering and building applications in C++, Java, and more recently in Scala/Spark, and has held several managerial positions. His interests include real-time analytics, modeling, and the development of nonlinear models.

See other products by R. Nicolas

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