Randomized algorithms
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
A step forward in studying the compact genetic algorithm
Evolutionary Computation
Comparison-based algorithms are robust and randomized algorithms are anytime
Evolutionary Computation
Computational complexity and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Lower Bounds for Evolution Strategies Using VC-Dimension
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Computational complexity and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Computational complexity and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
General lower bounds for evolutionary algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Diversity loss in general estimation of distribution algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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Estimation of distribution algorithms (EDAs) try to solve an optimization problem by finding a probability distribution focussed around its optima. For this purpose they conduct a sampling-evaluation-adjustment cycle, where search points are sampled with respect to a probability distribution, which is adjusted according to the evaluation of the sampled points. Although there are many successful experiments suggesting the usefulness of EDAs, there are only few rigorous theoretical results apart from convergence results without time bounds. Here we present first rigorous runtime analyses of a simple EDA, the compact genetic algorithm, for linear pseudo-boolean functions on n variables. We prove a number of results showing that not all linear functions have the same asymptotical runtime.