Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/133422
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Type: Journal article
Title: Toward more efficient heuristic construction of Boolean functions
Author: Jakobovic, D.
Picek, S.
Martins, M.S.R.
Wagner, M.
Citation: Applied Soft Computing, 2021; 107:1-15
Publisher: Elsevier BV
Issue Date: 2021
ISSN: 1568-4946
1872-9681
Statement of
Responsibility: 
Domagoj Jakobovic, Stjepan Picek, Marcella S.R. Martins, Markus Wagner
Abstract: Boolean functions have numerous applications in domains as diverse as coding theory, cryptography,and telecommunications. Heuristics play an important role in the construction of Boolean functions with the desired properties for a specific purpose. However, there are only sparse results trying to understand the problem’s difficulty. With this work, we aim to address this issue. We conduct a fitness landscape analysis based on Local Optima Networks (LONs) and investigate the influence of different optimization criteria and variation operators. We observe that the naive fitness formulation results in the largest networks of local optima with disconnected components. Also, the combination of variation operators can both increase or decrease the network size. Most importantly, we observe correlations of local optima’s fitness, their degrees of interconnection, and the sizes of the respective basins of attraction. This can be exploited to restart algorithms dynamically and influence the degree of perturbation of the current best solution when restarting.
Keywords: Balancedness; Nonlinearity; Landscape analysis; Local optima networks
Rights: © 2021ElsevierB.V.Allrightsreserved.
DOI: 10.1016/j.asoc.2021.107327
Grant ID: http://purl.org/au-research/grants/arc/DE160100850
http://purl.org/au-research/grants/arc/DP200102364
http://purl.org/au-research/grants/arc/DP210102670
Appears in Collections:Computer Science publications

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