Evolutionary Optimization in Noisy Environments
Bibliography
Evolutionary optimization has often to be conducted in the presence
of noise. Generally, two sorts of noise have been considered:
 Noise in the fintee function. In this case, we mean the noise is
additive and, in most cases unbiased. Research on this kind of
noise focus mainly on how to reduce the influence of the noise. Two
main approaches are available: explicit averaging and implicit avaraging.
 Noise in the design variables or in the environmental parameters.
When noise is present in designvariables or in environmental parameters,
the main motivation is to find an optimal solution that is insensitive
to noises. This is often known as search for robust solutions.
The robustness can be defined based on the expected fitness
given a certain distribution of the noise or the worstcase fitness,
given a threshold of tolerance in performance or in design variables.
In both cases, an important issue is to reduce the additional computational
cost in dealing with the nosie. To this end, adaptive sampling (change
of sample size, use of ad hoc sample techniques instead of random sampling),
introduction of local search and use of metamodels have been
studied.
In evolutionary optimization, ad hoc methods, such as fitness
inheritance, fitness imitation and fitness assignment can also be employed.
References on handling approximate fitness functions can be found
here.
A survey of the research can be found here.
Please send me an email to include your papers (in bibtex format)
in this bibliography. Thanks.
Last updated on August 12, 2005 by Yaochu Jin.
All publications sorted by year


K. deb and H. Gupta.
Searching for robust Paretooptimal solutons in multiobjective optimization.
In Evolutionary MultiCriterion Optimization,
LNCS 3410,
pages 150164.
Springer,
2005.
[bibtexkey = Deb05]

J. Hu,
X. Zhong,
and E. Goodman.
Openend robust design of analog filters using genetic programming.
In Genetic and Evolutionary Computation Conference,
pages 16191626,
2005.
[bibtexkey = Hu05]

M. Li,
S. Azarm,
and V. Aute.
A multiobjective genetic algorithm for robust design optimization.
In Genetic and Evolutionary Computation Conference,
pages 771778,
2005.
[bibtexkey = Li05]

I. Paenke,
J. Branke,
and Y. Jin.
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation.
IEEE Transactions on Evolutionary Computation,
2004.
Note: To appear.
[bibtexkey = PBJ04]

H.G. Beyer.
Actuator noise in recominant evolution strategies on general quadratic fitness models.
In K. Deb et.al., editor,
Genetic and Evolutionary Computation Conference,
volume 3102 of LNCS,
pages 654665,
2004.
Springer.
[bibtexkey = Bey04]

J. Branke and C. Schmidt.
Sequential Sampling in Noisy Environments.
In Parallel Problem Solving from Nature,
LNCS,
2004.
Springer.
[bibtexkey = BrSc04b]

E. CantuPaz.
Adaptive sampling for noisy problems.
In Genetic and Evolutionary Computation Conference,
pages 947958,
2004.
Springer.
[bibtexkey = Can04]

A. Di Pietro,
L. While,
and L. Barone.
Applying evolutionary algorithms to problems with noisy, timeconsuming fitness functions.
In Congress on Evolutionary Computation,
pages 12541261,
2004.
IEEE.
[bibtexkey = DWL04]

S. Droste.
Analysis of the $(1+1)$ EA for a noisy onemax.
In K. Deb et.al., editor,
Genetic and Evolutionary Computation Conference,
volume 3102 of LNCS,
pages 10881099,
2004.
Springer.
[bibtexkey = Dro04]

D. V. Arnold and H.G. Beyer.
A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise.
Computational Optimization and Applications,
24:135159,
2003.
[bibtexkey = ArBe03]

R. C. Ball,
T. M. A. Fink,
and N. E. Bowler.
Stochastic Annealing.
Physical Review Letters,
91,
2003.
Note: 030201.
[bibtexkey = BFB03]

J. Branke and C. Schmidt.
Selection in the presence of noise.
In E. CantuPaz, editor,
Genetic and Evolutionary Computation Conference,
volume 2723 of LNCS,
pages 766777,
2003.
Springer.
[bibtexkey = BrSc03]

W. Gutjahr.
A converging ACO algorithm for stochastic combinatorial optimization.
In A. Albrecht and K. Steinhoefl, editors,
Stochastic Algorithms: Foundations and Applications,
volume 2827 of LNCS,
pages 1025,
2003.
Springer.
[bibtexkey = Gut03]

Y. Jin and B. Sendhoff.
Tradeoff between performance and robustness: An evolutionary multiobjective approach.
In Evolutionary Multicriterion Optimization,
LNCS 2632,
pages 237251,
2003.
Springer.
[bibtexkey = JiSe03]

D. V. Arnold.
Noisy Optimization with Evolution Strategies.
Kluwer,
2002.
[bibtexkey = Arn02]

T. Beielstein and S. Markon.
Threshold selection, Hypothesis test, and DOE methods.
In Congress on Evolutionary Computation,
pages 777782,
2002.
IEEE.
[bibtexkey = BeMa02]

M. Giacobini,
M. Tomassini,
and L. Vanneschi.
Limiting the Number Fitness Cases in Genetic Programming Using Statistics.
In J.J. Merelo Guervos, editor,
Parallel Problem Solving from Nature,
volume 2439 of LNCS,
pages 371380,
2002.
Springer.
[bibtexkey = gtv02]

A. Gosavi.
The Effect of Noise on Artificial Intelligence and MetaHeuristic Techniques.
In Artificial Neural Networks in Engineering Conference,
volume 12,
pages 981988,
2002.
American Society of Mechanical Engineering Press.
[bibtexkey = Gos02]

T. Ray.
Constrained robust optimal design using a multiobjective evolutionary algorithm.
In Congress on Evolutionary Computation,
pages 419424,
2002.
IEEE.
[bibtexkey = Ray02]

Y. Sano and H. Kita.
Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation.
In Congress on Evolutionary Computation,
pages 360365,
2002.
IEEE.
[bibtexkey = SaKi02]

B. Sendhoff,
H.G. Beyer,
and M. Olhofer.
On noise induced multimodality in evolutionary algorithms.
In AsiaPacific Conference on Simulated Evolution and Learning,
volume 1,
pages 219224,
2002.
[bibtexkey = BBO02]

J. Branke.
Evolutionary Optimization in Dynamic Environments.
Kluwer,
2001.
[bibtexkey = Bra01]

D. Arnold.
Evolution strategies in noisy environments  A survey of existing work.
In L. Kallel,
B. Naudts,
and A. Rogers, editors,Theoretical Aspects of Evolutionary Computing,,
pages 239249.
Springer Verlag,
Heidelberg,
2001.
[bibtexkey = Arn01]

Phillip D. Stroud.
KalmanExtended Genetic Algorithm for Search in Nonstationary Environments with Noisy Fitness Evaluations.
IEEE Transactions on Evolutionary Computation,
5(1):6677,
2001.
[bibtexkey = str01]

J. Branke,
C. Schmidt,
and H. Schmeck.
Efficient fitness estimation in noisy environment.
In L. Spector et al., editor,
Genetic and Evolutionary Computation,
pages 243250,
2001.
Morgan Kaufmann.
[bibtexkey = BSS01]

J. Branke.
Reducing the Sampling Variance when Searching for Robust Solutions.
In L. Spector et al., editor,
Genetic and Evolutionary Computation Conference (GECCO '01),
pages 235242,
2001.
Morgan Kaufmann.
[bibtexkey = Branke:2001]

Evan J. Hughes.
Evolutionary Multiobjective Ranking with Uncertainty and Noise.
In E. Zitzler et al., editor,
Evolutionary MultiCriterion Optimization,
volume 1993 of LNCS,
pages 329343,
2001.
Springer.
[bibtexkey = Hug01]

S. Markon,
D.V. Arnold,
T. Bäck,
T. Beielstein,
and H.G. Beyer.
Thresholding  a selection operator for noisy ES.
In Congress on Evolutionary Computation,
pages 465472,
2001.
IEEE.
[bibtexkey = MABBB01]

G. Rudolph.
A Partial Order Approach to Noisy Fitness Functions.
In Congress on Evolutionary Computation,
pages 318325,
2001.
IEEE.
[bibtexkey = rud01]

J. Teich.
ParetoFront Exploration with Uncertain Objectives.
In E. Zitzler et al., editor,
Evolutionary MultiCriterion Optimization,
volume 1993 of LNCS,
pages 314328,
2001.
Springer.
[bibtexkey = Tei01]

S. Baumert and R.L. Smith.
Pure random search for noisy objective functions.
Technical report 0103,
Department of Industrial and Operations Engineering, The University of Michigan,
2001.
[bibtexkey = BaSm01]

H.G. Beyer.
Evolutionary Algorithms in Noisy Environments: Theoretical Issues and Guidelines for Practice.
Computer methods in applied mechanics and engineering,
186:239267,
2000.
[bibtexkey = Bey00]

I. Das.
Robustness optimization for constrained nonlinear programming problems.
Engineering Optimization,
32(5):585618,
2000.
[bibtexkey = Das00]

D. V. Arnold and H.G. Beyer.
Local Performance of the $(\mu/\mu_I,\lambda)$ES in a Noisy Environment.
In W. Martin and W. Spears, editors,
Foundations of Genetic Algorithms,
pages 127142,
2000.
Morgan Kaufmann.
[bibtexkey = ArBe00b]

D. V. Arnold and H.G. Beyer.
Efficiency and Mutation Strength Adaptation of the $(\mu/\mu_I,\lambda)$ES in a Noisy Environment.
In M. Schoenauer et al., editor,
Parallel Problem Solving from Nature,
volume 1917 of LNCS,
pages 3948,
2000.
Springer.
[bibtexkey = ArBe00]

Y. Sano,
H. Kita,
I. Kamihira,
and M. Yamaguchi.
Online Optimization of an Engine Controller by means of a Genetic Algorithm using History of Search.
In AsiaPacific Conference on Simulated Evolution and Learning,
pages 29292934,
2000.
Springer.
[bibtexkey = SKKY00]

Y. Sano and H. Kita.
Optimization of Noisy Fitness Functions by Means of Genetic Algorithms Using History of Search.
In M. Schoenauer et al., editor,
Parallel Problem Solving from Nature,
volume 1917 of LNCS,
pages 571580,
2000.
Springer.
[bibtexkey = SaKi00]

C. Bierwirth and D. C. Mattfeld.
Production Scheduling and Rescheduling with Genetic Algorithms.
Evolutionary Computation,
7(1):118,
1999.
[bibtexkey = BiMa99]

P. Darwen and J. Pollack.
CoEvolutionary learning on noisy tasks.
In Congress on Evolutionary Computation,
pages 17241731,
1999.
IEEE.
[bibtexkey = DaPo99]

D. Costa and E. A. Silver.
Tabu Search When Noise is Present: An Illustration in the Context of Cause and Effect Analysis.
Journal of Heuristics,
4:523,
1998.
[bibtexkey = CoSi98]

D. Wiesmann,
U. Hammel,
and T. Bäck.
Robust design of multilayer optical coatings by means of evolutionary algorithms.
IEEE Transactions on Evolutionary Computation,
2(4):162167,
1998.
[bibtexkey = WHB98]

J. Branke.
Creating robust solutions by means of evolutionary algorithms.
In Parallel Problem Solving from Nature,
LNCS,
pages 119128,
1998.
Springer.
[bibtexkey = Bra98]

G. Rudolph.
Evolutionary search for minimal elements in partially ordered fitness sets.
In Annual Conference on Evolutionary Programming,
Berlin,
pages 345353,
1998.
Springer.
[bibtexkey = Rud98]

P. Stagge.
Averaging Efficiently in the Presence of Noise.
In A. E. Eiben et al., editor,
Parallel Problem Solving from Nature V,
volume 1498 of LNCS,
pages 188197,
1998.
Springer.
[bibtexkey = Sta98]

A. Thompson.
On the automatic design of robust electronics through artificial evolution.
In International Conference on Evolvable Systems,
pages 1324,
1998.
Springer.
[bibtexkey = Tho98]

B. L. Miller.
Noise, Sampling, and Efficient Genetic Algorithms.
PhD thesis,
Dept. of Computer Science, University of Illinois at UrbanaChampaign,
1997.
Note: Available as TR 97001.
[bibtexkey = Mi97]

De.E. Moriarty and R. Miikkulainen.
Forming neural networks through efficient and adaptive coevolution.
Evolutionary Computation,
5(4):373399,
1997.
[bibtexkey = MoMi97]

S. Tsutsui and A. Ghosh.
Genetic algorithms with a robust solution searching scheme.
IEEE Transactions on Evolutionary Computation,
1(3):201208,
1997.
[bibtexkey = TsGh97]

L. M. Rattray and J. Shapiro.
Noisy Fitness Evaluation in Genetic Algorithms and the Dynamics of Learning.
In R. K. Belew and M. D. Vose, editors,
Foundations of Genetic Algorithms,
pages 117139,
1997.
Morgan Kaufmann.
[bibtexkey = RaSh97]

H. Greiner.
Robust optical coating design with evolution strategies.
Applied Optics,
35(28):54775483,
1996.
[bibtexkey = Gre96]

Gutjahr W. J. and Pflug G. C..
Simulated annealing for noisy cost functions.
J. Global Optim.,
8(1):113,
1996.
[bibtexkey = GuPf96]

B. L. Miller and D. E. Goldberg.
Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise.
Evolutionary Computation,
4(2):113131,
1996.
[bibtexkey = MiGo97]

J. Redmond and G. Parker.
Actuator placement based on reachable set optimization for expected disturbance.
Journal Optimization Theory and Applications,
90(2):279300,
August 1996.
[bibtexkey = RePa96]

S. Rana,
L. D. Whitley,
and R. Cogswell.
Searching in the Presence of Noise.
In H.M. Voigt, editor,
Parallel Problem Solving from Nature,
volume 1141 of LNCS,
pages 198207,
1996.
Springer.
[bibtexkey = RWC96]

S. Tsutsui,
A. Ghosh,
and Y. Fujimoto.
A robust solution searching scheme in genetic search.
In Parallel Problem Solving from Nature,
pages 543552,
1996.
Springer.
[bibtexkey = TGF96]

P.J. Darwen and X. Yao.
On evolving robust strategies for iterated prisoner's dilemma.
In X. Yao, editor,Progress in Evolutionary Computation,
volume 956 of LNAI,
pages 276292.
Springer,
Berlin,
1995.
[bibtexkey = DaYa95]

E.B. Baum,
D. Boneh,
and C. Garret.
On genetic algorithms.
In 8th Annual Conference on Computational Learning Theory,
pages 230239,
1995.
Springer.
[bibtexkey = BBG95]

B.L. Miller and D.E. Goldberg.
Genetic algorithms, selection schemes and the varying effects of noise.
Technical report IlliGAL Report No. 95009,
Department of General Engineering, University of Illinoise at UrbanaChampaign,
1995.
[bibtexkey = MiGo95]

A. N. Aizawa and B. W. Wah.
Scheduling of Genetic Algorithms in a Noisy Environment.
Evolutionary Computation,
pp 97122,
1994.
[bibtexkey = AiWa94]

B. Levitan and S. Kauffman.
Adaptive Walks with Noisy Fitness Measurements.
Molecular Diversity,
1(1):5368,
1994.
[bibtexkey = LeKa95]

T. Bäck and U. Hammel.
Evolution strategies applied to perturbed objective functions.
In Congress on Evolutionary Computation,
pages 4045,
1994.
IEEE.
[bibtexkey = HB94a]

U. Hammel and T. Bäck.
Evolution Strategies on Noisy Functions, How to Improve Convergence Properties.
In Y. Davidor,
H. P. Schwefel,
and R. Männer, editors,
Parallel Problem Solving from Nature,
volume 866 of LNCS,
pages 159168,
1994.
Springer.
[bibtexkey = HB94]

H.G. Beyer.
Toward a Theory of Evolution Strategies: Some Asymptotical Results from the $(1\stackrel{+}{,}\lambda)$Theory.
Evolutionary Computation,
1(2):165188,
1993.
[bibtexkey = Bey93]

A.N. Aizawa and B.W Wah.
Dynamic control of genetic algorithms in a noisy environment.
In Conference on Genetic Algorithms,
pages 4855,
1993.
Morgan Kaufmann.
[bibtexkey = AiWa93]

D.E. Goldberg,
K. Deb,
and J.H. Clark.
Genetic algorithms, noise, and the sizing of populations.
Complex Systems,
6:333362,
1992.
[bibtexkey = GDC92]

J. J. Grefenstette.
Genetic algorithms for changing environments.
In R. Maenner and B. Manderick, editors,
Parallel Problem Solving from Nature,
pages 137144,
1992.
NorthHolland.
[bibtexkey = Gre92]

A.V. Sebald and D.B. Fogel.
Design of fault tolerant neural networks for pattern classification.
In D.B. Fogel and W. Atmar, editors,
Annual Conference on Evolutionary Programming,
pages 9099,
1992.
[bibtexkey = SeFo92]

J. M. Fitzpatrick and J. J. Grefenstette.
Genetic Algorithms in Noisy Environments.
Machine Learning,
3:101120,
1988.
[bibtexkey = FiGr88]
BACK TO INDEX
Disclaimer:
This material is presented to ensure timely dissemination of
scholarly and technical work. Copyright and all rights therein
are retained by authors or by other copyright holders.
All person copying this information are expected to adhere to
the terms and constraints invoked by each author's copyright.
In most cases, these works may not be reposted
without the explicit permission of the copyright holder.
Les documents contenus dans ces répertoires sont rendus disponibles
par les auteurs qui y ont contribué en vue d'assurer la diffusion
à temps de travaux savants et techniques sur une base noncommerciale.
Les droits de copie et autres droits sont gardés par les auteurs
et par les détenteurs du copyright, en dépit du fait qu'ils présentent
ici leurs travaux sous forme électronique. Les personnes copiant ces
informations doivent adhérer aux termes et contraintes couverts par
le copyright de chaque auteur. Ces travaux ne peuvent pas être
rendus disponibles ailleurs sans la permission explicite du détenteur
du copyright.
Last modified: Fri Aug 12 14:22:21 2005
Author: yaochu.
This document was translated from BibT_{E}X by
bibtex2html