Abstract
Machine learning is the discipline of learning from data and observations. It combines statistics and learning paradigms from artificial intelligence. This chapter introduces concepts to support Genetic Algorithms with machine learning. For a detailed introduction to this field see [56]. Machine learning evolved to a very successful area of research in the last decades.
Keywords
- Genetic Algorithm
- Fitness Function
- Dimensionality Reduction Method
- Covariance Matrix Estimation
- Covariance Matrix Adaptation Evolution Strategy
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Kramer, O. (2017). Machine Learning. In: Genetic Algorithm Essentials. Studies in Computational Intelligence, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-319-52156-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-52156-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52155-8
Online ISBN: 978-3-319-52156-5
eBook Packages: EngineeringEngineering (R0)