• Publications
  • Influence
A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms
  • Dirk Sudholt
  • Computer Science, Mathematics
  • IEEE Transactions on Evolutionary Computation
  • 7 September 2011
TLDR
A new method based on fitness-level partitions and an additional condition on transition probabilities between fitness levels allows us to determine the optimal mutation-based algorithm for LO and OneMax, i.e., the algorithm that minimizes the expected number of fitness evaluations. Expand
Adaptive population models for offspring populations and parallel evolutionary algorithms
We present two adaptive schemes for dynamically choosing the number of parallel instances in parallel evolutionary algorithms. This includes the choice of the offspring population size in a (1+λ) EAExpand
Unbiased Black-Box Complexity of Parallel Search
TLDR
A new black-box complexity model for search algorithms evaluating λ search points in parallel that captures the inertia caused by offspring populations in evolutionary algorithms and the total computational effort in parallel metaheuristics is proposed. Expand
How Crossover Speeds up Building Block Assembly in Genetic Algorithms
  • Dirk Sudholt
  • Mathematics, Computer Science
  • Evolutionary Computation
  • 26 March 2014
TLDR
For royal road functions and OneMax, it is shown that using crossover makes every (+) genetic algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderate  and . Expand
The choice of the offspring population size in the (1,λ) EA
TLDR
This work extends the theory of non-elitist evolutionary algorithms by considering the offspring population size in the (1,λ) EA and establishes a sharp threshold at λ that decreases with the mutation rate, illustrating the balance between selection and mutation. Expand
General Upper Bounds on the Runtime of Parallel Evolutionary Algorithms*
We present a general method for analyzing the runtime of parallel evolutionary algorithms with spatially structured populations. Based on the fitness-level method, it yields upper bounds on theExpand
On the analysis of the (1+1) memetic algorithm
  • Dirk Sudholt
  • Mathematics, Computer Science
  • GECCO '06
  • 8 July 2006
TLDR
This work introduces a simple memetic algorithm, the (1+1) Memetic Algorithm (1-1(MA), working with a population size of 1 and no crossover and defines a class of fitness functions where a small variation of the local search duration has a large impact on the performance of the ( 1-1) MA. Expand
On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization
TLDR
A rigorous runtime analysis concerning the update strength, a vital parameter in PMBGAs such as the step size 1 / K in the so-called compact Genetic Algorithm and the evaporation factor $$\rho $$ρ in ant colony optimizers (ACO). Expand
Crossover is provably essential for the Ising model on trees
  • Dirk Sudholt
  • Mathematics, Computer Science
  • GECCO '05
  • 25 June 2005
TLDR
Here, a more natural fitness function based on a generalized Ising model is presented where crossover is essential throughout the whole optimization process. Expand
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus
TLDR
This work extends their analyses of MMASbs to the class of unimodal functions and shows improved results for test functions using new and specialized techniques; in particular, it presents new lower bounds. Expand
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