Optimal sampling strategies for learning a fitness model

Ratle, Alain

doi:10.1109/cec.1999.785531

Cited by 54 publications

(30 citation statements)

References 13 publications

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“…• Metamodel assisted EA with periodic sampling (EA-PS): The algorithm leverages on the concepts by Ratle (1999) andde Jong (2006), where the accuracy of the metamodel is safeguarded by periodically evaluating a small subset of the EA population with the true objective function, and then incorporating them into the metamodel to improve its accuracy. The algorithm begins by generating an initial sample of vectors and evaluating them with the true expensive function.…”

Section: Performance Analysismentioning

confidence: 99%

Machine–Learning in Optimization of Expensive Black–Box Functions

Tenne

2017

International Journal of Applied Mathematics and Computer Science

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Modern engineering design optimization often uses computer simulations to evaluate candidate designs. For some of these designs the simulation can fail for an unknown reason, which in turn may hamper the optimization process. To handle such scenarios more effectively, this study proposes the integration of classifiers, borrowed from the domain of machine learning, into the optimization process. Several implementations of the proposed approach are described. An extensive set of numerical experiments shows that the proposed approach improves search effectiveness.

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Section: Performance Analysismentioning

confidence: 99%

Machine–Learning in Optimization of Expensive Black–Box Functions

Tenne

2017

International Journal of Applied Mathematics and Computer Science

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“…• Evolutionary algorithm with periodic sampling (EA-PS): The algorithm leverages on the concepts in references [24,25]. It uses a Kriging metamodel and a real-coded evolutionary algorithm (EA).…”

Section: Benchmark Tests Based On Mathematical Test Functionsmentioning

confidence: 99%

A Simulated Annealing Based Optimization Algorithm

Tenne¹

2017

Computational Optimization in Engineering - Paradigms and Applications

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“…MOPSA-EA allows for any kind of surrogates, and in this paper Artificial Neural Networks (ANNs) are being used since ANNs have been considered being appropriate for approximation of complex problems with limited number of data samples [6], [18]. The ANN adopted has a feed-forward architecture with one hidden layer.…”

Section: B Surrogatementioning

confidence: 99%

A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems

Syberfeldt

Grimm

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems. Computation, CEC 2008 (pp. 3177-3184 In: 2008 IEEE Congress on Evolutionary Preprint of paper published in Proceedings of IEEE Congress on Evolutionary Computation, 2008Abstract-This paper presents a new efficient multiobjective evolutionary algorithm for solving computationallyintensive optimization problems. To support a high degree of parallelism, the algorithm is based on a steady-state design. For improved efficiency the algorithm utilizes a surrogate to identify promising candidate solutions and filter out poor ones. To handle the uncertainties associated with the approximative surrogate evaluations, a new method for multi-objective optimization is described which is generally applicable to all surrogate techniques. In this method, basically, surrogate objective values assigned to offspring are adjusted to consider the error of the surrogate. The algorithm is evaluated on the ZDT benchmark functions and on a real-world problem of manufacturing optimization. In assessing the performance of the algorithm, a new performance metric is suggested that combines convergence and diversity into one single measure. Results from both the benchmark experiments and the realworld test case indicate the potential of the proposed algorithm.

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Optimal sampling strategies for learning a fitness model

Cited by 54 publications

References 13 publications

Machine–Learning in Optimization of Expensive Black–Box Functions

Machine–Learning in Optimization of Expensive Black–Box Functions

A Simulated Annealing Based Optimization Algorithm

A parallel surrogate-assisted multi-objective evolutionary algorithm for computationally expensive optimization problems

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