Local performance of the (1 + 1)-ES in a noisy environment

While noise is a phenomenon present in many real world optimization problems, the understanding of its potential effects on the performance of evolutionary algorithms is still incomplete. This paper investigates the effects of fitness proportionate Gaussian noise for a (1 + 1)-ES with isotropic norm...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2002-02, Vol.6 (1), p.30-41
Main Authors: Arnold, D.V., Beyer, H.-G.
Format: Article
Language:English
Subjects:
Additive noise
Applied sciences
Approximation
Artificial intelligence
Computer science
control theory
systems
Evolutionary algorithms
Evolutionary computation
Exact sciences and technology
Fitness
Gaussian
Gaussian noise
Genetic mutations
Mathematical analysis
Mathematical programming
Multi-stage noise shaping
Mutations
Noise
Noise generators
Noise measurement
Operational research and scientific management
Operational research. Management science
Optimization
Performance analysis
Speech and sound recognition and synthesis. Linguistics
Stochastic resonance
Working environment noise
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:While noise is a phenomenon present in many real world optimization problems, the understanding of its potential effects on the performance of evolutionary algorithms is still incomplete. This paper investigates the effects of fitness proportionate Gaussian noise for a (1 + 1)-ES with isotropic normal mutations on the quadratic sphere in the limit of infinite search-space dimensionality. It is demonstrated experimentally that the results provide a good approximation for finite space dimensionality. It is shown that overvaluation as a result of failure to re-evaluate parental fitness leads to both reduced success probabilities and improved performance. Implications for mutation strength adaptation rules are discussed and optimal re-sampling rates are computed.
ISSN:1089-778X
1941-0026
DOI:10.1109/4235.985690