Simulated Evolution and Learning [electronic resource] : 10th International Conference, SEAL 2014, Dunedin, New Zealand, December 15-18, Proceedings /

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Material type: Text

TextSeries: Theoretical Computer Science and General Issues ; 8886Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Edition: 1st ed. 2014Description: XVI, 862 p. 267 illus. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319135632

Subject(s):

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

004.0151 23

LOC classification:

QA75.5-76.95

Online resources:

Click here to access online

Contents:

Evolutionary optimization -- Evolutionary multi-objective optimization -- Evolutionary machine learning -- Theoretical developments -- Evolutionary feature reduction -- Evolutionary scheduling and combinatorial optimization -- Real world applications and evolutionary image analysis.

In: Springer Nature eBookSummary: This volume constitutes the proceedings of the 10th International Conference on Simulated Evolution and Learning, SEAL 2012, held in Dunedin, New Zealand, in December 2014. The 42 full papers and 29 short papers presented were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on evolutionary optimization; evolutionary multi-objective optimization; evolutionary machine learning; theoretical developments; evolutionary feature reduction; evolutionary scheduling and combinatorial optimization; real world applications and evolutionary image analysis.

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This volume constitutes the proceedings of the 10th International Conference on Simulated Evolution and Learning, SEAL 2012, held in Dunedin, New Zealand, in December 2014. The 42 full papers and 29 short papers presented were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on evolutionary optimization; evolutionary multi-objective optimization; evolutionary machine learning; theoretical developments; evolutionary feature reduction; evolutionary scheduling and combinatorial optimization; real world applications and evolutionary image analysis.

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