Machine Learning for Evolution Strategies (Studies in Big by Oliver Kramer

By Oliver Kramer

This book
introduces a variety of algorithmic hybridizations among either worlds that show
how desktop studying can increase and help evolution techniques. The set of
methods includes covariance matrix estimation, meta-modeling of health and
constraint services, dimensionality aid for seek and visualization of
high-dimensional optimization methods, and clustering-based niching. After
giving an creation to evolution thoughts and computer studying, the book
builds the bridge among either worlds with an algorithmic and experimental
perspective. Experiments often hire a (1+1)-ES and are carried out in Python
using the computing device studying library scikit-learn. The examples are carried out on
typical benchmark difficulties illustrating algorithmic recommendations and their
experimental habit. The e-book closes with a dialogue of comparable strains of
research.

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