Comparison of the set found when solving IM for k = 19 by the greedy algorithm (left) and the one found by (1 + 1)-WEA for a network with n = 50 vertices.

Vertices from the target set are marked red, active vertices—by blue and inactive vertices are gray. It is clear that the set found using (1 + 1)-WEA manages to activate 11 more vertices out of 50 compared to the set found by the greedy algorithm.

Gorde:

Xehetasun bibliografikoak
Egile nagusia:	Alexander Andreev (16822333) (author)
Beste egile batzuk:	Stepan Kochemazov (677091) (author), Alexander Semenov (677092) (author)
Argitaratua:	2025
Gaiak:	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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Comparison of the set found when solving IM for <i>k</i> = 19 by the greedy algorithm (left) and the one found by (1 + 1)-WEA for a network with <i>n</i> = 50 vertices.

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