Learning and Intelligent Optimization [electronic resource] : 15th International Conference, LION 15, Athens, Greece, June 20–25, 2021, Revised Selected Papers /

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Material type: Text

TextSeries: Theoretical Computer Science and General Issues ; 12931Publisher: Cham : Springer International Publishing : Imprint: Springer, 2021Edition: 1st ed. 2021Description: XIII, 410 p. 115 illus., 84 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783030921217

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Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:

518 23

LOC classification:

QA71-90

Online resources:

Click here to access online

In: Springer Nature eBookSummary: This book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 15, held in Athens, Greece, in June 2021. The 30 full papers presented have been carefully reviewed and selected from 35 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components.

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This book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 15, held in Athens, Greece, in June 2021. The 30 full papers presented have been carefully reviewed and selected from 35 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components.

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