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Scala for Machine Learning

You're reading from   Scala for Machine Learning Leverage Scala and Machine Learning to construct and study systems that can learn from data

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783558742
Length 624 pages
Edition 1st Edition
Languages
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Author (1):
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Patrick R. Nicolas Patrick R. Nicolas
Author Profile Icon Patrick R. Nicolas
Patrick R. Nicolas
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Table of Contents (15) Chapters Close

Preface 1. Getting Started 2. Hello World! FREE CHAPTER 3. Data Preprocessing 4. Unsupervised Learning 5. Naïve Bayes Classifiers 6. Regression and Regularization 7. Sequential Data Models 8. Kernel Models and Support Vector Machines 9. Artificial Neural Networks 10. Genetic Algorithms 11. Reinforcement Learning 12. Scalable Frameworks A. Basic Concepts Index

Implementation


As mentioned earlier, the genetic operators are independent of the problem to be solved. Let's implement all the components of the reproduction cycle. The fitness function and the encoding scheme are highly domain specific.

In accordance with the principles of object-oriented programming, the software architecture defines the genetic operators using a top-down approach: starting with the population, then each chromosome, and down to each gene.

Software design

The implementation of the genetic algorithm uses a design that is similar to the template for classifiers (refer to the Design template for classifier section in the Appendix A, Basic Concepts).

The key components of the implementation of the genetic algorithm are as follows:

  • The Population class defines the current set of solution candidates or chromosomes.

  • The GASolver class implements the GA solver and has two components: a configuration object of the GAConfig type and the initial population. This class implements an explicit...

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