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Theoretical Foundations of Immune-Inspired Randomized Search Heuristics for Optimization [PDF]

, 2019
Artificial immune systems are a class of nature-inspired algorithms based on the immune system of vertebrates. They have been used in a large number of different areas of application, most prominently learning, classification, pattern recognition, and (function) optimization.
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Local search heuristics for multi-index assignment problems with decomposable costs. [PDF]

The multi-index assignment problem (MIAP) with decomposable costs is a natural generalization of the well-known assignment problem. Applications of the MIAP arise for instance in the field of multi-target multi-sensor tracking.
Bandelt, H, Maas, A, Spieksma, Frederik
core

Finding large cliques in sparse semi-random graphs by simple randomized search heuristics

Theoretical Computer Science, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Finding large cliques in sparse semi-random graphs by simple randomized search heuristics

, 2006
Surprisingly, general heuristics often solve hard combinatorial problems quite sufficiently, although they do not outperform specialized algorithms. Here, the behavior of simple randomized optimizers on the maximum clique problem is investigated. We focus on semi-random models for sparse graphs, in which an adversary is even allowed to insert a limited
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Learning to explore tree neighbourhoods for phylogenetic inference. [PDF]