Convergence plots for solving IM for p2p-Gnutella06_uni_1-1000_uni_0.75-1 via (1 + 1)-WEA.

<p>The value of <i>k</i> corresponds to the size of the target set, the influence (vertical axis) refers to the number of activated vertices in a fixed point, while mutations (horizontal axis) means the number of mutations (iterations) made by the evolutionary algorithm.</p>

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Detalles Bibliográficos
Autor Principal:	Alexander Andreev (16822333) (author)
Outros autores:	Stepan Kochemazov (677091) (author), Alexander Semenov (677092) (author)
Publicado:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified target set selection fixed hamming weight construct initial solutions 1 )- ea computational scheme combining boolean hypercube comprised known influence maximization using evolutionary algorithms new algorithms outperform random networks constructed known greedy heuristic algorithm suit well evolutionary algorithms greedy heuristic combining greedy known models random graphs proposed algorithms proposed algorithm new variant maximize influence boolean optimization boolean networks xlink "> world networks main novelty fitness function classic variant also propose
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Convergence plots for solving IM for p2p-Gnutella06_uni_1-1000_uni_0.75-1 via (1 + 1)-WEA.

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