Application of Sensitivity Analysis for an Improved Representation in Evolutionary Design Optimization

Graening, Lars; Menzel, Stefan; Ramsay, Thomas; Sendhoff, Bernhard

doi:10.1109/icgec.2012.64

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…2. For example, history data can be used to determine the most effective and compact representation of a very large scale complex problem [29]. We also want to note that domain knowledge about the problem structure or information about the search performance acquired in the optimization process can be incorporated or re-used in EAs to enhance the evolutionary search performance.…”

Section: Data-driven Evolutionary Optimizationmentioning

confidence: 99%

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Jin

Wang

Chugh

et al. 2019

IEEE Trans. Evol. Computat.

443

144

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Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.

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Section: Data-driven Evolutionary Optimizationmentioning

confidence: 99%

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Jin

Wang

Chugh

et al. 2019

IEEE Trans. Evol. Computat.

443

144

View full text Add to dashboard Cite

show abstract

“…Information-theoretic methods, such as the mutual information (MI), are model-free and are able to capture dependencies of arbitrary order, while requiring only minimal assumptions about the data for their estimation when using state-of-the-art estimators [5]. These properties make information-theoretic measures particularly promising tools for the analysis of data in the engineering domain [6], for example, results from optimization runs [7].…”

Section: Introductionmentioning

confidence: 99%

Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory

Schmitt¹

2021

Preprint

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An important issue during an engineering design process is to develop an understanding which design parameters have the most influence on the performance. Especially in the context of optimization approaches this knowledge is crucial in order to realize an efficient design process and achieve highperforming results. Information theory provides powerful tools to investigate these relationships because measures are model-free and thus also capture non-linear relationships, while requiring only minimal assumptions on the input data. We therefore propose to use recently introduced information-theoretic methods and estimation algorithms to find the most influential input parameters in optimization results. The proposed methods are in particular able to account for interactions between parameters, which are often neglected but may lead to redundant or synergistic contributions of multiple parameters. We demonstrate the application of these methods on optimization data from aerospace engineering, where we first identify the most relevant optimization parameters using a recently introduced information-theoretic feature-selection algorithm that accounts for interactions between parameters. Second, we use the novel partial information decomposition (PID) framework that allows to quantify redundant and synergistic contributions between selected parameters with respect to the optimization outcome to identify parameter interactions. We thus demonstrate the power of novel information-theoretic approaches in identifying relevant parameters in optimization runs and highlight how these methods avoid the selection of redundant parameters, while detecting interactions that result in synergistic contributions of multiple parameters.

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Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory

Schmitt

2021

2021 IEEE Symposium Series on Computational Intelligence (SSCI)

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Application of Sensitivity Analysis for an Improved Representation in Evolutionary Design Optimization

Cited by 3 publications

References 9 publications

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory

Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory

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