Fitness Distance Correlation and Ridge Functions
PPSN V Proceedings of the 5th 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
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Theoretical analysis of fitness-proportional selection: landscapes and efficiency
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A few ants are enough: ACO with iteration-best update
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
General lower bounds for the running time of evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
General scheme for analyzing running times of parallel evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Algorithmica - Special Issue: Theory of Evolutionary Computation
Fitness-levels for non-elitist populations
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Optimizing expected path lengths with ant colony optimization using fitness proportional update
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Lessons from the black-box: fast crossover-based genetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A method to derive fixed budget results from expected optimisation times
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Improved runtime analysis of the simple genetic algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
How the (1+λ) evolutionary algorithm optimizes linear functions
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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We extend the theory of non-elitist evolutionary algorithms (EAs) by considering the offspring population size in the (1,λ) EA. We establish a sharp threshold at λ = log{\frac{e}{e-1}} n ≈5 log10 n between exponential and polynomial running times on OneMax. For any smaller value, the (1,λ) EA needs exponential time on every function that has only one global optimum. We also consider arbitrary unimodal functions and show that the threshold can shift towards larger offspring population sizes. Finally, we investigate the relationship between the offspring population size and arbitrary mutation rates on OneMax. We get sharp thresholds for λ that decrease with the mutation rate. This illustrates the balance between selection and mutation.