activity
20122024
most citedFitness Probability Distribution of Bit-Flip Mutation

47 citations · 86 across the 11 of their papers we have counts for

collaborators

12 papers

cs.NE2024

Fixed-Parameter Tractability of the (1+1) Evolutionary Algorithm on Random Planted Vertex Covers

Jack Kearney, Frank Neumann, Andrew M. Sutton

We present the first parameterized analysis of a standard (1+1) Evolutionary Algorithm on a distribution of vertex cover problems. We show that if the planted cover is at most loga…

cs.NE2024

Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem

Denis Antipov, Aneta Neumann, Frank Neumann +1

Diversity optimization is the class of optimization problems in which we aim to find a diverse set of good solutions. One of the frequently-used approaches to solve such problems i…

cs.AI2023★ 8 cited

Rigorous Runtime Analysis of MOEA/D for Solving Multi-Objective Minimum Weight Base Problems

Anh Viet Do, Aneta Neumann, Frank Neumann +1

We study the multi-objective minimum weight base problem, an abstraction of classical NP-hard combinatorial problems such as the multi-objective minimum spanning tree problem. We p…

cs.NE2022

Focused Jump-and-Repair Constraint Handling for Fixed-Parameter Tractable Graph Problems Closed Under Induced Subgraphs

Luke Branson, Andrew M. Sutton

Repair operators are often used for constraint handling in constrained combinatorial optimization. We investigate the (1+1)~EA equipped with a tailored jump-and-repair operation th…

cs.DS2021★ 4 cited

Runtime Analysis of RLS and the (1+1) EA for the Chance-constrained Knapsack Problem with Correlated Uniform Weights

Yue Xie, Aneta Neumann, Frank Neumann +1

Addressing a complex real-world optimization problem is a challenging task. The chance-constrained knapsack problem with correlated uniform weights plays an important role in the c…

cs.NE2020★ 9 cited

Parameterized Complexity Analysis of Randomized Search Heuristics

Frank Neumann, Andrew M. Sutton

This chapter compiles a number of results that apply the theory of parameterized algorithmics to the running-time analysis of randomized search heuristics such as evolutionary algo…