Machine learning for evolution strategies
著者
書誌事項
Machine learning for evolution strategies
（Studies in big data, v. 20）
Springer, c2016
大学図書館所蔵 件 / 全2件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Part I Evolution Strategies.- Part II Machine Learning.- Part III Supervised Learning.
「Nielsen BookData」 より