Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Where Elitists Start Limping Evolution Strategies at Ridge Functions
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Invariance, Self-Adaptation and Correlated Mutations and Evolution Strategies
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Analysis of the (1, λ) - ES on the Parabolic Ridge
Evolutionary Computation
Analysis of the (μ/μ, λ) - ES on the Parabolic Ridge
Evolutionary Computation
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
On the performance of (1, λ)-evolution strategies for theridge function class
IEEE Transactions on Evolutionary Computation
Why noise may be good: additive noise on the sharp ridge
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Step length adaptation on ridge functions
Evolutionary Computation
A novel approach to adaptive isolation in evolution strategies
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Mutation strength control by meta-ES on the sharp ridge
Proceedings of the 14th annual conference on Genetic and evolutionary computation
On the behaviour of the (1,λ)-σSA-ES for a constrained linear problem
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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The progress rate of a self-adaptive evolution strategy is sub-optimal on ridge functions because the global step-size, denoted σ, becomes too small. On the parabolic ridge we conjecture that σ will stabilize when selection is unbiased towards larger or smaller step-sizes. On the sharp ridge, where the bias in selection is constant, σ will continue to decrease. We show that this is of practical interest because ridges can cause even the best solutions found by self-adaptation to be of little value on ridge problems where spatially close parameters tend to have similar values.