TBU Publications
Repository of TBU Publications

Detecting potential design weaknesses in shade through network feature analysis

DSpace Repository

Title: Detecting potential design weaknesses in shade through network feature analysis
Author: Viktorin, Adam; Pluháček, Michal; Šenkeřík, Roman; Kadavý, Tomáš
Document type: Conference paper (English)
Source document: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017, vol. 10334 LNCS, p. 662-673
ISSN: 0302-9743 (Sherpa/RoMEO, JCR)
Journal Impact This chart shows the development of journal-level impact metrics in time
JCR Journal Citation Reports - Impact Factor
SNIP SCImago Source Normalized Impact per Paper
SJR SCImago Journal Rank
IPP SCImago Impact per Publication
CS Scopus CiteScore
(Note: The metrics are not comparable against one another.)
ISBN: 978-3-319-59650-1
DOI: https://doi.org/10.1007/978-3-319-59650-1_56
Abstract: This preliminary study presents a hybridization of two research fields – evolutionary algorithms and complex networks. A network is created by the dynamic of an evolutionary algorithm, namely Success-History based Adaptive Differential Evolution (SHADE). Network feature, node degree centrality, is used afterward to detect potential design weaknesses of SHADE algorithm. This approach is experimentally tested on the CEC2015 benchmark set of test functions and future directions in the research are proposed. © Springer International Publishing AG 2017.
Full text: https://link.springer.com/chapter/10.1007/978-3-319-59650-1_56
Show full item record

Find Full text Export to RefWorks
   

 

Files in this item

Icon
Name: Fulltext_1007261.pdf
Size: 938.0Kb
Format: PDF
Description: fulltext

ČSN ISO 690:2011 citation

Citace článku v konferenčním sborníku:
VIKTORIN, Adam, Michal PLUHÁČEK, Roman ŠENKEŘÍK a Tomáš KADAVÝ. Detecting potential design weaknesses in shade through network feature analysis. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [online]. Logroño (La Rioja): Springer Verlag, 2017, s. 662-673. [cit. 2024-04-23]. ISSN 0302-9743. Dostupné z: https://link.springer.com/chapter/10.1007/978-3-319-59650-1_56.

These citations are software generated and may contain errors. To verify accuracy, check the appropriate style guide.

Related articles