C. Witt

发表

On the Optimization of Monotone Polynomials by the (1+1) EA and Randomized Local Search

I. Wegener, C. Witt, 2003, GECCO.

Runtime Analysis of a (μ +1) ES for the Sphere Function

C. Witt, J. Jägersküpper, 2006 .

UNIVERSITY OF DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Ant Colony Optimization and the Minimum Spanning Tree Problem

C. Witt, F. Neumann, 2006 .

An analysis of the (u+1) EA on pseudo-boolean functions

C. Witt, 2004 .

Population size vs. runtime of a simple EA

C. Witt, 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

On the Behavior of the (1+1) Evolutionary Algorithm on Quadratic Pseudo-Boolean Functions

I. Wegener, C. Witt, 2001 .

Runtime Analysis of the ( μ +1) EA on Simple Pseudo-Boolean Functions

Carsten Witt, C. Witt, 2006 .

Runtime Analysis for Self-adaptive Mutation Rates

Benjamin Doerr, C. Witt, Jing Yang, 2020, Algorithmica.

The Interplay of Population Size and Mutation Probability in the (1+λ) EA on OneMax

Carsten Witt, Christian Gießen, C. Witt, 2017, Algorithmica.

Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods Runtime Analysis of Binary Pso Runtime Analysis of Binary Pso *

C. Witt, Dirk Sudholt, 2022 .

Population Size vs . Runtime of a Simple Evolutionary Algorithm

C. Witt, 2003 .

Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods On Improving Approximate Solutions by Evolutionary Algorithms

Jun He, C. Witt, T. Friedrich, 2022 .

Implementation and Evaluation of Estimation-of-Distribution Algorithms

C. Witt, N. Larsen, 2017 .

GECCO ’14: Proceedings of the 2014 conference on Genetic and evolutionary computation

Julian Togelius, Christine Zarges, Hitoshi Iba, 2014 .

How majority-vote crossover and estimation-of-distribution algorithms cope with fitness valleys

C. Witt, 2022, Theor. Comput. Sci..

Stagnation detection in highly multimodal fitness landscapes

C. Witt, A. Rajabi, 2021, GECCO.

Stagnation Detection with Randomized Local Search*

C. Witt, A. Rajabi, 2021, Evolutionary Computation.

The ($$1+\lambda $$1+λ) Evolutionary Algorithm with Self-Adjusting Mutation Rate

Benjamin Doerr, C. Witt, Jing Yang, 2018, Algorithmica.

On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization

C. Witt, Dirk Sudholt, 2018, Algorithmica.

Upper Bounds on the Running Time of the Univariate Marginal Distribution Algorithm on OneMax

C. Witt, 2017, Algorithmica.

The Interplay of Population Size and Mutation Probability in the ($$1+\lambda $$1+λ) EA on OneMax

C. Witt, C. Gießen, 2017, Algorithmica.

Theory of Randomized Search Heuristics

A. Auger, C. Witt, 2012, Algorithmica.

Greedy Local Search and Vertex Cover in Sparse Random Graphs

Carsten Witt, C. Witt, 2009, TAMC.

Rigorous Runtime Analysis of Swarm Intelligence Algorithms – An Overview

Carsten Witt, C. Witt, 2009 .

Theoretical Properties of Two ACO Approaches for the Traveling Salesman Problem

Frank Neumann, Carsten Witt, Timo Kötzing, 2010, ANTS Conference.

On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization

Dirk Sudholt, Carsten Witt, C. Witt, 2018, Algorithmica.

Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions†

Carsten Witt, C. Witt, 2013, Combinatorics, Probability and Computing.

On crossing fitness valleys with majority-vote crossover and estimation-of-distribution algorithms

C. Witt, 2021, FOGA.

On the analysis of a simple evolutionary algorithm on quadratic pseudo-boolean functions

Ingo Wegener, Carsten Witt, I. Wegener, 2005, J. Discrete Algorithms.

The Complex Parameter Landscape of the Compact Genetic Algorithm

Dirk Sudholt, Carsten Witt, Johannes Lengler, 2020, Algorithmica.

Evolutionary Algorithms with Self-adjusting Asymmetric Mutation

C. Witt, A. Rajabi, 2020, PPSN.

Medium step sizes are harmful for the compact genetic algorithm

Dirk Sudholt, Carsten Witt, Johannes Lengler, 2018, GECCO.

Upper Bounds on the Running Time of the Univariate Marginal Distribution Algorithm on OneMax

Carsten Witt, C. Witt, 2018, Algorithmica.

The Impact of a Sparse Migration Topology on the Runtime of Island Models in Dynamic Optimization

Carsten Witt, Andrei Lissovoi, C. Witt, 2017, Algorithmica.

Lower Bounds on the Run Time of the Univariate Marginal Distribution Algorithm on OneMax

Carsten Witt, Martin S. Krejca, C. Witt, 2017, FOGA '17.

Update Strength in EDAs and ACO: How to Avoid Genetic Drift

Dirk Sudholt, Carsten Witt, C. Witt, 2016, GECCO.

Improved time complexity analysis of the Simple Genetic Algorithm

Pietro Simone Oliveto, Carsten Witt, C. Witt, 2015, Theor. Comput. Sci..

Improved runtime analysis of the simple genetic algorithm

Pietro Simone Oliveto, Carsten Witt, C. Witt, 2013, GECCO '13.

Analysis of an iterated local search algorithm for vertex cover in sparse random graphs

Carsten Witt, C. Witt, 2012, Theor. Comput. Sci..

Why standard particle swarm optimisers elude a theoretical runtime analysis

Carsten Witt, C. Witt, 2009, FOGA '09.

Population size versus runtime of a simple evolutionary algorithm

Carsten Witt, C. Witt, 2008, Theor. Comput. Sci..

Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods Runtime Analysis of a Simple Ant Colony Optimization Algorithm Runtime Analysis of a Simple Ant Colony Optimization Algorithm

Frank Neumann, Carsten Witt, C. Witt, 2022 .

Bioinspired computation in combinatorial optimization: algorithms and their computational complexity

Frank Neumann, Carsten Witt, C. Witt, 2010, GECCO '12.

Theory of estimation-of-distribution algorithms

Carsten Witt, Martin S. Krejca, C. Witt, 2018, GECCO.

Runtime analysis of binary PSO

Dirk Sudholt, Carsten Witt, C. Witt, 2008, GECCO '08.

Design and Management of Complex Technical Processes and Systems by Means of Computational Intelligence Methods Theoretical Analysis of Diversity Mechanisms for Global Exploration Theoretical Analysis of Diversity Mechanisms for Global Exploration

Pietro Simone Oliveto, Dirk Sudholt, Tobias Friedrich, 2022 .

On the runtime analysis of the 1-ANT ACO algorithm

Dirk Sudholt, Frank Neumann, Benjamin Doerr, 2007, GECCO '07.

On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best

Dogan Corus, Carsten Witt, Andrei Lissovoi, 2021, ACM Trans. Evol. Learn. Optim..

The compact genetic algorithm struggles on Cliff functions

C. Witt, Dirk Sudholt, F. Neumann, 2022, GECCO.

Simulated annealing is a polynomial-time approximation scheme for the minimum spanning tree problem

Benjamin Doerr, C. Witt, A. Rajabi, 2022, GECCO.

Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables

C. Witt, F. Neumann, 2021, IJCAI.

Self-Adjusting Evolutionary Algorithms for Multimodal Optimization

Carsten Witt, Amirhossein Rajabi, C. Witt, 2020, Algorithmica.

Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm

C. Witt, Dirk Sudholt, P. S. Oliveto, 2021, Algorithmica.

Tail bounds on hitting times of randomized search heuristics using variable drift analysis

P. K. Lehre, C. Witt, C. Witt, 2020, Comb. Probab. Comput..

Runtime Analysis for Self-adaptive Mutation Rates

Benjamin Doerr, C. Witt, Jing Yang, 2020, Algorithmica.

Runtime Analysis for Self-adaptive Mutation Rates

Benjamin Doerr, Jing Yang, Carsten Witt, 2018, Algorithmica.

Domino convergence: why one should hill-climb on linear functions

Carsten Witt, C. Witt, 2018, GECCO.

The (1+λ) evolutionary algorithm with self-adjusting mutation rate

Benjamin Doerr, Jing Yang, Carsten Witt, 2017, GECCO.

A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization

Carsten Witt, Andrei Lissovoi, C. Witt, 2016, Algorithmica.

On the Utility of Island Models in Dynamic Optimization

Carsten Witt, Andrei Lissovoi, C. Witt, 2015, GECCO.

On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling

Frank Neumann, Carsten Witt, C. Witt, 2015, IJCAI.

(1+1) EA on Generalized Dynamic OneMax

Carsten Witt, Timo Kötzing, Andrei Lissovoi, 2015, FOGA.

MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions

Carsten Witt, Andrei Lissovoi, C. Witt, 2014, Algorithmica.

Concentrated Hitting Times of Randomized Search Heuristics with Variable Drift

Per Kristian Lehre, Carsten Witt, C. Witt, 2014, ISAAC.

On the runtime analysis of the Simple Genetic Algorithm

Pietro Simone Oliveto, Carsten Witt, C. Witt, 2014, Theor. Comput. Sci..

Fitness levels with tail bounds for the analysis of randomized search heuristics

Carsten Witt, C. Witt, 2014, Inf. Process. Lett..

A method to derive fixed budget results from expected optimisation times

Thomas Jansen, Christine Zarges, Benjamin Doerr, 2013, GECCO '13.

Runtime analysis of ant colony optimization on dynamic shortest path problems

Carsten Witt, Andrei Lissovoi, C. Witt, 2013, GECCO '13.

When do evolutionary algorithms optimize separable functions in parallel?

Dirk Sudholt, Benjamin Doerr, Carsten Witt, 2013, FOGA XII '13.

Sharp bounds by probability-generating functions and variable drift

Mahmoud Fouz, Benjamin Doerr, Carsten Witt, 2011, GECCO '11.

Quasirandom evolutionary algorithms

Mahmoud Fouz, Benjamin Doerr, Carsten Witt, 2010, GECCO '10.

A few ants are enough: ACO with iteration-best update

Dirk Sudholt, Frank Neumann, Carsten Witt, 2010, GECCO '10.

Black-Box Search by Unbiased Variation

Per Kristian Lehre, Carsten Witt, C. Witt, 2010, GECCO '10.

Approximating Covering Problems by Randomized Search Heuristics Using Multi-Objective Models

Carsten Witt, Nils Hebbinghaus, Frank Neumann, 2007, Evolutionary Computation.

Analysis of Diversity-Preserving Mechanisms for Global Exploration

Pietro Simone Oliveto, Dirk Sudholt, Tobias Friedrich, 2009, Evolutionary Computation.

Runtime Analysis of the ( + 1) EA on Simple Pseudo-Boolean Functions

Carsten Witt, C. Witt, 2006, Evolutionary Computation.

On the Optimization of Monotone Polynomials by Simple Randomized Search Heuristics

Ingo Wegener, Carsten Witt, I. Wegener, 2005, Combinatorics, Probability and Computing.

Sharp bounds on the runtime of the (1+1) EA via drift analysis and analytic combinatorial tools

Hsien-Kuei Hwang, Carsten Witt, C. Witt, 2019, FOGA '19.

Bioinspired Computation in Combinatorial Optimization

Carsten Witt, Frank Neumann, C. Witt, 2010, Bioinspired Computation in Combinatorial Optimization.

Improved Runtime Results for Simple Randomised Search Heuristics on Linear Functions with a Uniform Constraint

Mojgan Pourhassan, Frank Neumann, Carsten Witt, 2019, Algorithmica.

Runtime analysis of a binary particle swarm optimizer

Dirk Sudholt, Carsten Witt, C. Witt, 2010, Theor. Comput. Sci..

Analysis of different MMAS ACO algorithms on unimodal functions and plateaus

Dirk Sudholt, Carsten Witt, Frank Neumann, 2009, Swarm Intelligence.

Optimal Mutation Rates for the (1+λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document}) EA on One

Carsten Witt, Christian Gießen, C. Witt, 2017, Algorithmica.

Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation

Pietro Simone Oliveto, Carsten Witt, C. Witt, 2008, Algorithmica.

Population Size vs. Mutation Strength for the (1+λ) EA on OneMax

Carsten Witt, Christian Gießen, C. Witt, 2015, GECCO.

3-Objective Pareto Optimization for Problems with Chance Constraints

C. Witt, F. Neumann, 2023, GECCO.

Revised analysis of the (1+1) ea for the minimum spanning tree problem

Carsten Witt, C. Witt, 2014, GECCO.

Analyses of Simple Hybrid Algorithms for the Vertex Cover Problem

Frank Neumann, Tobias Friedrich, Carsten Witt, 2009, Evolutionary Computation.

UNIVERSITY OF DORTMUND REIHE COMPUTATIONAL INTELLIGENCE COLLABORATIVE RESEARCH CENTER 531 Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Worst-Case and Average-Case Approximations by Simple Randomized Search Heuristics

Carsten Witt, C. Witt, 2004 .

General Drift Analysis with Tail Bounds pdf

Per Kristian Lehre, Carsten Witt, C. Witt, 2013, ArXiv.

Theory of Particle Swarm Optimization

Carsten Witt, C. Witt, 2011, Theory of Randomized Search Heuristics.

A tight lower bound on the expected runtime of standard steady state genetic algorithms

Dirk Sudholt, Carsten Witt, Pietro S. Oliveto, 2020, GECCO.

Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions

Dirk Sudholt, Frank Neumann, Carsten Witt, 2007, SLS.

Minimum Spanning Trees Made Easier

Frank Neumann, Carsten Witt, C. Witt, 2010 .

Computational Complexity of Evolutionary Computation in Combinatorial Optimisation

Frank Neumann, Carsten Witt, C. Witt, 2008 .

Fast Pareto Optimization Using Sliding Window Selection

C. Witt, F. Neumann, 2023, ArXiv.

Population Size vs . Runtime of a Simple Evolutionary Algorithm

C. Witt, 2003 .

Rigorous runtime analysis of a (μ+1)ES for the sphere function

Jens Jägersküpper, Carsten Witt, C. Witt, 2005, GECCO '05.

Runtime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions

C. Witt, F. Neumann, 2022, PPSN.

Lower Bounds on the Runtime of Crossover-Based Algorithms via Decoupling and Family Graphs

Andrew M. Sutton, Carsten Witt, C. Witt, 2019, Algorithmica.

Optimal Mutation Rates for the (1+$$\lambda $$λ) EA on OneMax Through Asymptotically Tight Drift Analysis

C. Witt, C. Gießen, 2018, Algorithmica.

Tight Bounds on the Optimization Time of the (1+1) EA on Linear Functions pdf

Carsten Witt, C. Witt, 2011, ArXiv.

Optimizing Linear Functions with Randomized Search Heuristics - The Robustness of Mutation

Carsten Witt, C. Witt, 2012, STACS.

Theoretical analysis of fitness-proportional selection: landscapes and efficiency

Pietro Simone Oliveto, Frank Neumann, Carsten Witt, 2009, GECCO.

Combinatorial Optimization and Computational Complexity

Frank Neumann, Carsten Witt, C. Witt, 2010 .

A Note on Problem Difficulty Measures in Black-Box Optimization: Classification, Realizations and Predictability

Xin Yao, Colin R. Reeves, Carsten Witt, 2007, Evolutionary Computation.

Runtime analysis of binary PSO

C. Witt, Dirk Sudholt, 2008, Annual Conference on Genetic and Evolutionary Computation.

An Analysis of the (µ+1) EA on Simple Pseudo-Boolean Functions

Carsten Witt, C. Witt, 2004, GECCO.

Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation

C. Witt, P. S. Oliveto, 2008 .

On the Behavior of the (1+1) Evolutionary Algorithm on Quadratic Pseudo-Boolean Functions

I. Wegener, C. Witt, 2001 .

Theory of estimation-of-distribution algorithms

Carsten Witt, C. Witt, 2020, GECCO Companion.

Theory of estimation-of-distribution algorithms

Carsten Witt, C. Witt, 2019, GECCO.

Erratum: Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation

C. Witt, P. S. Oliveto, 2012, 1211.7184.

On the analysis of the simple genetic algorithm

Pietro Simone Oliveto, Carsten Witt, C. Witt, 2012, GECCO '12.

Improved Fixed-Budget Results via Drift Analysis pdf

Carsten Witt, Timo Kötzing, Timo Kötzing, 2020, PPSN.

Estimation-of-Distribution Algorithms: Theory and Applications

Vicente P. Soloviev, Benjamin Doerr, C. Witt, 2022 .

On improving approximate solutions by evolutionary algorithms

Frank Neumann, Tobias Friedrich, Carsten Witt, 2007 .

Collaborative Research Centre 531: Computational Intelligence – Theory and Practice (Sonderforschungsbereich 531: Computational Intelligence – Theorie und Praxis)

Carsten Witt, Jörn Mehnen, Thomas Michelitsch, 2007, it Inf. Technol..

Theory of randomised search heuristics in combinatorial optimisation

Carsten Witt, C. Witt, 2010, GECCO '10.

For Peer Review Analysis of Scalability in Population-based Evolutionary Algorithms for Linear-like Fitness Functions

X. Yao, C. Witt, 2010 .

Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation

C. Witt, P. S. Oliveto, 2008 .