The time complexity of maximum matching by simulated annealing
Journal of the ACM (JACM)
Randomized algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
On mixing times for stratified walks on the d-cube
Random Structures & Algorithms
Rigorous hitting times for binary mutations
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
On the local performance of simulated annealing and the (1+1) evolutionary algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Theoretical Computer Science
Population size versus runtime of a simple evolutionary algorithm
Theoretical Computer Science
Convergence Analysis of Evolution Strategies with Random Numbers of Offspring
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Evolutionary Computation
Analyses of simple hybrid algorithms for the vertex cover problem*
Evolutionary Computation
Theoretical analysis of fitness-proportional selection: landscapes and efficiency
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analysis of diversity-preserving mechanisms for global exploration*
Evolutionary Computation
When to use bit-wise neutrality
Natural Computing: an international journal
A self-stabilizing algorithm for cut problems in synchronous networks
Theoretical Computer Science
Comparing variants of MMAS ACO algorithms on pseudo-boolean functions
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
Nonlinear network optimization: an embedding vector space approach
IEEE Transactions on Evolutionary Computation
Tight analysis of the (1+1)-ea for the single source shortest path problem
Evolutionary Computation
Revisiting the restricted growth function genetic algorithm for grouping problems
Evolutionary Computation
Worst-case and average-case approximations by simple randomized search heuristics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
The use of tail inequalities on the probable computational time of randomized search heuristics
Theoretical Computer Science
Crossover speeds up building-block assembly
Proceedings of the 14th annual conference on Genetic and evolutionary computation
When do evolutionary algorithms optimize separable functions in parallel?
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Black-box complexity: from complexity theory to playing mastermind
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Randomized search heuristics like evolutionary algorithms and simulated annealing find many applications, especially in situations where no full information on the problem instance is available. In order to understand how these heuristics work, it is necessary to analyse their behaviour on classes of functions. Such an analysis is performed here for the class of monotone pseudo-Boolean polynomials. Results depending on the degree and the number of terms of the polynomial are obtained. The class of monotone polynomials is of special interest since simple functions of this kind can have an image set of exponential size, improvements can increase the Hamming distance to the optimum and, in order to find a better search point, it can be necessary to search within a large plateau of search points with the same fitness value.