Simulated annealing: theory and applications
Simulated annealing: theory and applications
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Randomized algorithms
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
The metropolis algorithm for graph bisection
Discrete Applied Mathematics
A computational view of population genetics
Random Structures & Algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Stochastic Hillclimbing as a Baseline Method for
Stochastic Hillclimbing as a Baseline Method for
How mutation and selection solve long-path problems in polynomial expected time
Evolutionary Computation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
How to analyse evolutionary algorithms
Theoretical Computer Science - Natural computing
On the Choice of the Mutation Probability for the (1+1) EA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Theoretical Aspects of Evolutionary Algorithms
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
On the Expected Runtime and the Success Probability of Evolutionary Algorithms
WG '00 Proceedings of the 26th International Workshop on Graph-Theoretic Concepts in Computer Science
Towards an analytic framework for analysing the computation time of evolutionary algorithms
Artificial Intelligence
When a genetic algorithm outperforms hill-climbing
Theoretical Computer Science
Real royal road functions: where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
Theoretical Computer Science
Crossover can provably be useful in evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Real royal road functions-where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
Analysis of the (1+1) EA for a dynamically bitwise changing ONEMAX
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Benefits of a population: five mechanisms that advantage population-based algorithms
IEEE Transactions on Evolutionary Computation
Learning the large-scale structure of the MAX-SAT landscape using populations
IEEE Transactions on Evolutionary Computation
Crossover can provably be useful in evolutionary computation
Theoretical Computer Science
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There is a lot of experimental evidence that crossover is, for some functions, an essential operator of evolutionary algorithms. Nevertheless, it was an open problem to prove for some function that an evolutionary algorithm using crossover is essentially more efficient than evolutionary algorithms without crossover. In this paper, such an example is presented and its properties are proved.