Machine Learning for Evolution Strategies

Serie: Studies in Big Data 20

This book
introduces numerous algorithmic hybridizations between both worlds that show
how machine learning can improve and support evolution strategies. The set of
methods comprises covariance matrix estimation, meta-modeling of fitness and
constraint functions, dimensionality reduction for search and visualization of
high-dimensional optimization processes, and clustering-based niching. Les mer
Vår pris
1856,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager

Paperback
Legg i
Paperback
Legg i
Vår pris: 1856,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager

Om boka

This book
introduces numerous algorithmic hybridizations between both worlds that show
how machine learning can improve and support evolution strategies. The set of
methods comprises covariance matrix estimation, meta-modeling of fitness and
constraint functions, dimensionality reduction for search and visualization of
high-dimensional optimization processes, and clustering-based niching. After
giving an introduction to evolution strategies and machine learning, the book
builds the bridge between both worlds with an algorithmic and experimental
perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python
using the machine learning library scikit-learn. The examples are conducted on
typical benchmark problems illustrating algorithmic concepts and their
experimental behavior. The book closes with a discussion of related lines of
research.

Fakta

Innholdsfortegnelse

Part I Evolution Strategies.- Part II Machine Learning.- Part III Supervised Learning.