Real royal road functions for constant population size
Theoretical Computer Science
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
The one-dimensional Ising model: mutation versus recombination
Theoretical Computer Science
Properties of symmetric fitness functions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A building-block royal road where crossover is provably essential
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Population size versus runtime of a simple evolutionary algorithm
Theoretical Computer Science
Real royal road functions for constant population size
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Benefits of a population: five mechanisms that advantage population-based algorithms
IEEE Transactions on Evolutionary Computation
Analysis of (1+1) evolutionary algorithm and randomized local search with memory
Evolutionary Computation
How crossover helps in pseudo-boolean optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An analysis on recombination in multi-objective evolutionary optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Crossover speeds up building-block assembly
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An analysis on recombination in multi-objective evolutionary optimization
Artificial Intelligence
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Many experiments have proved that crossover is an essential search operator in evolutionary algorithms, at least for certain functions. However, the rigorous analysis of such algorithms on crossover-friendly functions is still in its infancy. Here, a recombinative hill-climber is analyzed on the crossover-friendly function hierarchical-if-and-only-if (H-IFF) introduced by Watson et al. (1998). The dynamics of this algorithm are investigated and it is proved that the expected optimization time equals Θ(n log n).