Book Image

Scala for Machine Learning

By : Patrick R. Nicolas
Book Image

Scala for Machine Learning

By: Patrick R. Nicolas

Overview of this book

Table of Contents (20 chapters)
Scala for Machine Learning
About the Author
About the Reviewers

Chapter 10. Genetic Algorithms

This chapter introduces the concept of evolutionary computing. Algorithms derived from the theory of evolution are particularly efficient in solving large combinatorial or NP problems. Evolutionary computing has been pioneered by John Holland [10:1] and David Goldberg [10:2]. 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 theoretical foundation of genetic algorithms

  • Advantages and limitations of genetic algorithms

From a practical perspective, you will learn how to:

  • Apply genetic algorithms to leverage 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 addressing

  • Tune some of the genetic operators

  • Create and evaluate fitness functions