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Iterative optimizers = difficulty me...

Clerc, Maurice.

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Iterative optimizers = difficulty measures and benchmarks /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Iterative optimizers/ Maurice Clerc.
其他題名:	difficulty measures and benchmarks /
作者:	Clerc, Maurice.
出版者:	London :ISTE ; : 2019.,
面頁冊數:	1 online resource :ill. (some col.)
標題:	Mathematical optimization. -
電子資源:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119612476
ISBN:	9781119612476

Iterative optimizers = difficulty measures and benchmarks /
Clerc, Maurice.

Iterative optimizersdifficulty measures and benchmarks /[electronic resource] :Maurice Clerc. - 1st ed. - London :ISTE ;2019. - 1 online resource :ill. (some col.) - Computer engineering series. - Computer engineering series (London, England).

Includes bibliographical references and index.

Almost every month, a new optimization algorithm is proposed, often accompanied by the claim that it is superior to all those that came before it. However, this claim is generally based on the algorithm's performance on a specific set of test cases, which are not necessarily representative of the types of problems the algorithm will face in real life. This book presents the theoretical analysis and practical methods (along with source codes) necessary to estimate the difficulty of problems in a test set, as well as to build bespoke test sets consisting of problems with varied difficulties. The book formally establishes a typology of optimization problems, from which a reliable test set can be deduced. At the same time, it highlights how classic test sets are skewed in favor of different classes of problems, and how, as a result, optimizers that have performed well on test problems may perform poorly in real life scenarios.

ISBN: 9781119612476Subjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Iterative optimizers = difficulty measures and benchmarks /
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245 1 0 $a Iterative optimizers $h [electronic resource] : $b difficulty measures and benchmarks / $c Maurice Clerc.
250 $a 1st ed.
260 $a London : $b ISTE ; $a Hoboken, NJ : $b John Wiley & Sons, $c 2019.
300 $a 1 online resource : $b ill. (some col.)
490 1 $a Computer engineering series
504 $a Includes bibliographical references and index.
520 $a Almost every month, a new optimization algorithm is proposed, often accompanied by the claim that it is superior to all those that came before it. However, this claim is generally based on the algorithm's performance on a specific set of test cases, which are not necessarily representative of the types of problems the algorithm will face in real life. This book presents the theoretical analysis and practical methods (along with source codes) necessary to estimate the difficulty of problems in a test set, as well as to build bespoke test sets consisting of problems with varied difficulties. The book formally establishes a typology of optimization problems, from which a reliable test set can be deduced. At the same time, it highlights how classic test sets are skewed in favor of different classes of problems, and how, as a result, optimizers that have performed well on test problems may perform poorly in real life scenarios.
588 $a Description based on print version record.
650 0 $a Mathematical optimization. $3 517763
830 0 $a Computer engineering series (London, England) $3 2148402
856 4 0 $u https://onlinelibrary.wiley.com/doi/book/10.1002/9781119612476