Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
The theory of evolution strategies
The theory of evolution strategies
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Noisy Local Optimization with Evolution Strategies
Noisy Local Optimization with Evolution Strategies
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Genetic Algorithms in Noisy Environments
Machine Learning
Evolution Strategies on Noisy Functions: How to Improve Convergence Properties
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
On the benefits of populations for noisy optimization
Evolutionary Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
The steady state behavior of (µ/µI, λ)-ES on ellipsoidal fitness models disturbed by noise
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
On the robustness of population-based versus point-basedoptimization in the presence of noise
IEEE Transactions on Evolutionary Computation
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Noise is a common problem encountered in real-world optimization. Although it is folklore that evolution strategies perform well in the presence of noise, even their performance is degraded. One effect on which we will focus in this paper is the reaching of a steady state that deviates from the actual optimal solution. The quality gain is a local progress measure, describing the expected one-generation change of the fitness of the population. It can be used to derive evolution criteria and steady state conditions which can be utilized as a starting point to determine the final fitness error, i.e. the expected difference between the actual optimal fitness value and that of the steady state. We will demonstrate the approach by determining the final solution quality for two fitness functions.