The theory of evolution strategies
The theory of evolution strategies
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Noisy Local Optimization with Evolution Strategies
Noisy Local Optimization with Evolution Strategies
Optimization with Noisy Function Evaluations
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Analysis of the (1, λ) - ES on the Parabolic Ridge
Evolutionary Computation
Toward a theory of evolution strategies: The (μ, λ)-theory
Evolutionary Computation
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Local performance of the (1 + 1)-ES in a noisy environment
IEEE Transactions on Evolutionary Computation
Expected sample moments of concomitants of selected order statistics
Statistics and Computing
Population size versus runtime of a simple evolutionary algorithm
Theoretical Computer Science
An evolutionary method for complex-process optimization
Computers and Operations Research
Viewing the problem from different angles: a new diversity measure based on angular distances
Journal of Artificial Evolution and Applications
An efficient evolutionary algorithm for solving incrementally structured problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On the prediction of the solution quality in noisy optimization
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
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It is known that, in the absence of noise, no improvement in local performance can be gained from retaining candidate solutions other than the best one. Yet, it has been shown experimentally that, in the presence of noise, operating with a nonsingular population of candidate solutions can have a marked and positive effect on the local performance of evolution strategies. So as to determine the reasons for the improved performance, we have studied the evolutionary dynamics of the (µ λ)-ES in the presence of noise. Considering a simple, idealized environment, we have developed a moment-based approach that uses recent results involving concomitants of selected order statistics. This approach yields an intuitive explanation for the performance advantage of multi-parent strategies in the presence of noise. It is then shown that the idealized dynamic process considered does bear relevance to optimization problems in high-dimensional search spaces.