عنوان

Contemporary evolution strategies /

(Number (ISBN

3642401376 (electronic bk.)

(Number (ISBN

9783642401374 (electronic bk.)

Erroneous ISBN

9783642401367

Number

dltt

Title Proper

Contemporary evolution strategies /

General Material Designation

[Book]

First Statement of Responsibility

Thomas Bäck, Christophe Foussette, Peter Krause

Specific Material Designation and Extent of Item

1 online resource (xiii, 90 pages) :

Other Physical Details

illustrations (some color)

Series Title

Natural computing series,

ISSN of Series

1619-7127

Text of Note

Includes bibliographical references

Text of Note

Evolution Strategies -- Taxonomy of Evolution Strategies -- Empirical Analysis -- Summary

0

Text of Note

Evolution strategies have more than 50 years of history in the field of evolutionary computation. Since the early 1990s, many algorithmic variations of evolution strategies have been developed, characterized by the fact that they use the so-called derandomization concept for strategy parameter adaptation. Most importantly, the covariance matrix adaptation strategy (CMA-ES) and its successors are the key representatives of this group of contemporary evolution strategies. This book provides an overview of the key algorithm developments between 1990 and 2012, including brief descriptions of the algorithms, a unified pseudocode representation of each algorithm, and program code which is available for download. In addition, a taxonomy of these algorithms is provided to clarify similarities and differences as well as historical relationships between the various instances of evolution strategies. Moreover, due to the authors' focus on industrial applications of nonlinear optimization, all algorithms are empirically compared on the so-called BBOB (Black-Box Optimization Benchmarking) test function suite, and ranked according to their performance. In contrast to classical academic comparisons, however, only a very small number of objective function evaluations is permitted. In particular, an extremely small number of evaluations, such as between one hundred and one thousand for high-dimensional functions, is considered. This is motivated by the fact that many industrial optimization tasks do not permit more than a few hundred evaluations. Our experiments suggest that evolution strategies are powerful nonlinear direct optimizers even for challenging industrial problems with a very small budget of function evaluations

Title

OhioLINK electronic book center (Online)

Title

SpringerLink

Evolutionary computation

Evolutionary programming (Computer science)

COM051300

UMB

Number

Edition

Class number

Class number

Bäck, Thomas,1963-

Foussette, Christophe

Krause, Peter

Ohio Library and Information Network

Date of Transaction

20140103091107.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y