%0 Journal Article %1 beyer:2004:GPEM %A Beyer, Hans-Georg %A Olhofer, Markus %A Sendhoff, Bernhard %D 2004 %J Genetic Programming and Evolvable Machines %K ES, analysis, evolution noisy optimisation, optimization performance robust strategies, %N 4 %P 327--360 %R doi:10.1023/B:GENP.0000036020.79188.a0 %T On the Impact of Systematic Noise on the Evolutionary Optimization Performance -- A Sphere Model Analysis %V 5 %X Quality evaluations in optimisation processes are frequently noisy. In particular evolutionary algorithms have been shown to cope with such stochastic variations better than other optimization algorithms. So far mostly additive noise models have been assumed for the analysis. However, we will argue in this paper that this restriction must be relaxed for a large class of applied optimization problems. We suggest systematic noise as an alternative scenario, where the noise term is added to the objective parameters or to environmental parameters inside the fitness function. We thoroughly analyse the sphere function with systematic noise for the evolution strategy with global intermediate recombination. The progress rate formula and a measure for the efficiency of the evolutionary progress lead to a recommended ratio between mu and lambda. Furthermore, analysis of the dynamics identifies limited regions of convergence dependent on the normalized noise strength and the normalised mutation strength. A residual localisation error Rinfin can be quantified and a second mu to lambda ratio is derived by minimising Rinfin.

@article{beyer:2004:GPEM, abstract = {Quality evaluations in optimisation processes are frequently noisy. In particular evolutionary algorithms have been shown to cope with such stochastic variations better than other optimization algorithms. So far mostly additive noise models have been assumed for the analysis. However, we will argue in this paper that this restriction must be relaxed for a large class of applied optimization problems. We suggest systematic noise as an alternative scenario, where the noise term is added to the objective parameters or to environmental parameters inside the fitness function. We thoroughly analyse the sphere function with systematic noise for the evolution strategy with global intermediate recombination. The progress rate formula and a measure for the efficiency of the evolutionary progress lead to a recommended ratio between [mu] and [lambda]. Furthermore, analysis of the dynamics identifies limited regions of convergence dependent on the normalized noise strength and the normalised mutation strength. A residual localisation error R[infin] can be quantified and a second [mu] to [lambda] ratio is derived by minimising R[infin].}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Beyer, Hans-Georg and Olhofer, Markus and Sendhoff, Bernhard}, biburl = {https://www.bibsonomy.org/bibtex/27c3eb0bc6a6c3085faf86e5089a168fb/brazovayeye}, doi = {doi:10.1023/B:GENP.0000036020.79188.a0}, interhash = {35effa8d2f57cf8d1dde15ac844279e3}, intrahash = {7c3eb0bc6a6c3085faf86e5089a168fb}, issn = {1389-2576}, journal = {Genetic Programming and Evolvable Machines}, keywords = {ES, analysis, evolution noisy optimisation, optimization performance robust strategies,}, month = {December}, notes = {Article ID: 5272968}, number = 4, pages = {327--360}, timestamp = {2008-06-19T17:36:33.000+0200}, title = {On the Impact of Systematic Noise on the Evolutionary Optimization Performance -- {A} Sphere Model Analysis}, volume = 5, year = 2004 }