Toward a theory of evolution strategies: Some asymptotical results from the (1,+ λ)-theory
Evolutionary Computation
Scheduling of genetic algorithms in a noisy environment
Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
Why noise may be good: additive noise on the sharp ridge
Proceedings of the 10th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Selection in the presence of noise
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Using the uncertainty handling CMA-ES for finding robust optima
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Evolutionary Algorithms are believed to be relatively robust on noisy objective functions, but generally stagnate in the (later) stages of the evolution process when the population has zoomed in on a particular area of the search space when the noise ratio becomes too large compared to the differences in fitness. The occurrence of stagnation in the search process has been proven for Evolution Strategies using a constant number of repeated samples (resampling size) to evaluate individuals. To prevent stagnation and speed-up convergence, a straightforward and appealing idea is to use the Student's t-test for deciding on the number of individuals to sample in one generation. This paper seeks to study this strategy for (1,lambda)-ES on the noisy sphere model. Besides showing gains achieved with such an adaptive approach in the early stage of runs we also show its limitations: Stagnation cannot be prevented in the long run. Additional studies aim to explain these results.