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Aneta Neumann

51 papers hereh-index 201.1k citations82 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author12
  • middle author38

Across the 50 of 51 papers where every author was matched, so the position is known.

fields
  • cs.NE35
  • cs.DS6
  • cs.AI3
  • cs.LG3
  • cs.CR1
  • cs.GR1
same name
  • Aneta Neumann — 22 papers, h 6
  • Aneta Neumann — 7 papers, h 1
  • Aneta Neumann — 2 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162026
most citedPareto Optimization for Subset Selection with Dynamic Cost Constraints

61 citations · 242 across the 48 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.NE2018

Evolutionary Diversity Optimization Using Multi-Objective Indicators

Aneta Neumann, Wanru Gao, Markus Wagner +1

Evolutionary diversity optimization aims to compute a diverse set of solutions where all solutions meet a given quality criterion. With this paper, we bridge the areas of evolution…

cs.DS2018★ 61 cited

Pareto Optimization for Subset Selection with Dynamic Cost Constraints

Vahid Roostapour, Aneta Neumann, Frank Neumann +1

We consider the subset selection problem for function f with constraint bound B that changes over time. Within the area of submodular optimization, various greedy approaches ar…

cs.NE2018★ 1 cited

Evolution of Images with Diversity and Constraints Using a Generator Network

Aneta Neumann, Christo Pyromallis, Bradley Alexander

Evolutionary search has been extensively used to generate artistic images. Raw images have high dimensionality which makes a direct search for an image challenging. In previous wor…

cs.NE2018

Discrepancy-based Evolutionary Diversity Optimization

Aneta Neumann, Wanru Gao, Carola Doerr +2

Diversity plays a crucial role in evolutionary computation. While diversity has been mainly used to prevent the population of an evolutionary algorithm from premature convergence,…

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