• Publications
  • Influence
Black-Box Search by Unbiased Variation
TLDR
This paper introduces a more restricted black-box model for optimisation of pseudo-Boolean functions which it is claimed captures the working principles of many randomised search heuristics including simulated annealing, evolutionary algorithms, randomised local search, and others. Expand
Runtime Analysis of the ( + 1) EA on Simple Pseudo-Boolean Functions
  • C. Witt
  • Computer Science, Medicine
  • Evolutionary Computation
  • 2006
TLDR
A newproof technique is developed that bounds the runtime of the ( + 1) EA and investigates the stochastic process for creating family trees of individuals; the depth of these trees is bounded and the progress of the population towards the optimum is captured. Expand
Runtime Analysis of the ( μ +1) EA on Simple Pseudo-Boolean Functions
Although Evolutionary Algorithms (EAs) have been successfully applied to optimization in discrete search spaces, theoretical developments remain weak, in particular for population-based EAs. ThisExpand
Runtime Analysis of a Simple Ant Colony Optimization Algorithm
TLDR
This work presents the first runtime analysis of an ACO algorithm, which transfers many rigorous results with respect to the runtime of a simple evolutionary algorithm to the authors' algorithm, and examines the choice of the evaporation factor, a crucial parameter in ACO algorithms, in detail. Expand
Bioinspired computation in combinatorial optimization: algorithms and their computational complexity
TLDR
The presenters show how runtime behavior can be analyzed in a rigorous way, in particular for combinatorial optimization, and show how multiobjective optimization can help to speed up bioinspired computation for single-objectives optimization problems. Expand
Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions†
  • C. Witt
  • Computer Science, Mathematics
  • Combinatorics, Probability and Computing
  • 9 January 2013
TLDR
The standard mutation probability p = 1/n is optimal for all linear functions, and the (1+1) EA is found to be an optimal mutation-based algorithm that turns out to be surprisingly robust since the large neighbourhood explored by the mutation operator does not disrupt the search. Expand
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
TLDR
The present paper picks up Hajek's line of thought to prove a drift theorem that is very easy to use in evolutionary computation and shows how previous analyses involving the complicated theorem can be redone in a much simpler and clearer way. Expand
Worst-Case and Average-Case Approximations by Simple Randomized Search Heuristics
  • C. Witt
  • Computer Science
  • STACS
  • 24 February 2005
TLDR
An average-case analysis for two input distributions reveals that one RSH is convergent to optimality in polynomial time, and it is shown that for both RSHs, parallel runs yield a PRAS. Expand
Approximating Covering Problems by Randomized Search Heuristics Using Multi-Objective Models
TLDR
It is shown that optimal solutions can be approximated within a logarithmic factor of the size of the ground set, using the multi-objective approach, while the approximation quality obtainable by the single- objective approach in expected polynomial time may be arbitrarily bad. Expand
Bioinspired computation in combinatorial optimization: algorithms and their computational complexity
TLDR
The presenters show how runtime behavior can be analyzed in a rigorous way, in particular for combinatorial optimization, and show how multiobjective optimization can help to speed up bioinspired computation for single-objectives optimization problems. Expand
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