Evolutionary and adaptive synthesis methods
Formal engineering design synthesis
Advances in evolutionary computing
Genetic Programming and Evolvable Machines
A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise
Genetic Programming and Evolvable Machines
Open-ended robust design of analog filters using genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Inverse multi-objective robust evolutionary design optimization in the presence of uncertainty
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Fitness function for finding out robust solutions on time-varying functions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Robustness in multi-objective optimization using evolutionary algorithms
Computational Optimization and Applications
Reliability-based optimization using evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
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
An archive maintenance scheme for finding robust solutions
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Using the uncertainty handling CMA-ES for finding robust optima
Proceedings of the 13th annual conference on Genetic and evolutionary computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Artificial Intelligence Review
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Robustness is an important requirement for almost all kinds of products. This article shows how evolutionary algorithms can be applied for robust design based on the approach of Taguchi. To achieve a better understanding of the consequences of this approach, we first present some analytical results gained from a toy problem. As a nontrivial industrial application we consider the design of multilayer optical coatings (MOCs) most frequently used for optical filters. An evolutionary algorithm based on a parallel diffusion model and extended for mixed-integer optimization was able to compete with or even outperform traditional methods of robust MOC design. With respect to chromaticity, the MOC designs found by the evolutionary algorithm are substantially more robust to parameter variations than a reference design and therefore perform much better in the average case. In most cases, however, this advantage has to be paid for by a reduction in the average reflectance. The robust design approach outlined in this paper should be easily adopted to other application domains