Evolution strategies on noisy functions how to improve convergence properties

Hammel, Ulrich; Bäck, Thomas

doi:10.1007/3-540-58484-6_260

Cited by 59 publications

(26 citation statements)

References 2 publications

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“…Fitzpatrick and Grefenstette [FG88] conclude that for the genetic algorithm studied, it is better to increase the population size than the sample size. On the other hand, Beyer [Bey93] shows that for a (1, λ) evolution strategy on a simple sphere, one should increase the sample size rather than λ. Hammel and Bäck [HB94] confirm these results and empirically show that it also doesn't help to increase the parent population size µ. Finally, Arnold and Beyer [AB00a,AB00b] show analytically that for the simple sphere, increasing the parent population size µ is helpful in combination with intermediate multirecombination.…”

Section: Related Workmentioning

confidence: 77%

Selection in the Presence of Noise

Branke

Schmidt

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract.For noisy optimization problems, there is generally a trade-off between the effort spent to reduce the noise (in order to allow the optimization algorithm to run properly), and the number of solutions evaluated during optimization. However, for stochastic search algorithms like evolutionary optimization, noise is not always a bad thing. On the contrary, in many cases, noise has a very similar effect to the randomness which is purposefully and deliberately introduced e.g. during selection. Using the example of stochastic tournament selection, we show that the noise inherent in the optimization problem should be taken into account by the selection operator, and that one should not reduce noise further than necessary.

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Section: Related Workmentioning

confidence: 77%

Selection in the Presence of Noise

Branke

Schmidt

2003

Genetic and Evolutionary Computation — GECCO 2003

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“…On the other hand, Beyer [7] argues that in a (1, λ)-ES, the sample size should be increased, rather than λ. Hammel and Bäck [8] verify Beyer's result and show that there is no benefit of increasing the parent population size.…”

Section: Introductionmentioning

confidence: 78%

Adaptive Sampling for Noisy Problems

Cantú‐Paz

2004

Lecture Notes in Computer Science

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Abstract. The usual approach to deal with noise present in many realworld optimization problems is to take an arbitrary number of samples of the objective function and use the sample average as an estimate of the true objective value. The number of samples is typically chosen arbitrarily and remains constant for the entire optimization process. This paper studies an adaptive sampling technique that varies the number of samples based on the uncertainty of deciding between two individuals. Experiments demonstrate the effect of adaptive sampling on the final solution quality reached by a genetic algorithm and the computational cost required to find the solution. The results suggest that the adaptive technique can effectively eliminate the need to set the sample size a priori, but in many cases it requires high computational costs.

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“…In these cases, it is difficult to define a neighborhood for a given individual, since most other individuals have different numbers and types of components. A related research area to robust design focuses on evolving robustness in a noisy environment [31][32][33]. In these problems, the variation in the environment leads to uncertainty in the fitness function evaluation and the true fitness of a candidate solution needs to be evaluated based on sampling in multiple environments.…”

Section: Related Workmentioning

confidence: 99%

A Novel Evolutionary Algorithm for Designing Robust Analog Filters

Zou

2018

Algorithms

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Designing robust circuits that withstand environmental perturbation and device degradation is critical for many applications. Traditional robust circuit design is mainly done by tuning parameters to improve system robustness. However, the topological structure of a system may set a limit on the robustness achievable through parameter tuning. This paper proposes a new evolutionary algorithm for robust design that exploits the open-ended topological search capability of genetic programming (GP) coupled with bond graph modeling. We applied our GP-based robust design (GPRD) algorithm to evolve robust lowpass and highpass analog filters. Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA), our GPRD algorithm with a fitness criterion rewarding robustness, with respect to parameter perturbations, can evolve more robust filters than what was achieved through parameter tuning alone. We also find that inappropriate GA tuning may mislead the search process and that multiple-simulation and perturbed fitness evaluation methods for evolving robustness have complementary behaviors with no absolute advantage of one over the other.

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Evolution strategies on noisy functions how to improve convergence properties

Cited by 59 publications

References 2 publications

Selection in the Presence of Noise

Selection in the Presence of Noise

Adaptive Sampling for Noisy Problems

A Novel Evolutionary Algorithm for Designing Robust Analog Filters

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