Abstract
| Original language | English |
|---|---|
| Article number | 18888677 |
| Pages (from-to) | 674-681 |
| Number of pages | 8 |
| Journal | 2019 IEEE Congress on Evolutionary Computation (CEC) |
| DOIs | |
| Publication status | Published - 8 Aug 2019 |
| Event | 2019 IEEE Congress On Evolutionary Computation - Te Papa Tongarewa, Wellington, New Zealand Duration: 10 Jun 2019 → 13 Jun 2019 http://cec2019.org/ |
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Keywords
- global optimization
- surrogate-assisted optimization
- large-scale optimization
- high dimensional
- expensive and black-box problems
- cooperative co-evolutionary algorithm
- differential grouping
- evolutionary algorithm
Cite this
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A Surrogate-Assisted Cooperative Co-evolutionary Algorithm Using Recursive Differential Grouping as Decomposition Strategy. / Blanchard, Julien; Beauthier, Charlotte; Carletti, Timoteo.
In: 2019 IEEE Congress on Evolutionary Computation (CEC), 08.08.2019, p. 674-681.Research output: Contribution to journal › Article
TY - JOUR
T1 - A Surrogate-Assisted Cooperative Co-evolutionary Algorithm Using Recursive Differential Grouping as Decomposition Strategy
AU - Blanchard, Julien
AU - Beauthier, Charlotte
AU - Carletti, Timoteo
PY - 2019/8/8
Y1 - 2019/8/8
N2 - ooperative co-evolutionary algorithms, especiallythose able to uncover interaction structure between variables,have a great potential in optimizing large-scale problems. Nev-ertheless, they are expensive in terms of number of functionevaluations and this issue can be quite problematic whendealing with computationally expensive optimization problems.An effective approach to deal with such problems lies in theexploitation of surrogate models. The latter ones work as cheap-to-evaluate alternatives to the expensive function reducing thecomputational cost, while still providing improved designs. Thisprocess, called surrogate-assisted optimization, is very effective onsmall-dimensional problems but is not suitable to solve large-scaleproblems due to the curse of dimensionality. In this paper, a newalgorithm, taking benefit from cooperative coevolution and sur-rogate models, is introduced to efficiently solve high-dimensional,expensive and black-box problems. The proposed algorithm usesrecursive differential grouping to perform an accurate problemdecomposition. Experimental results are provided on a set of1000-dimensional problems and show promising results
AB - ooperative co-evolutionary algorithms, especiallythose able to uncover interaction structure between variables,have a great potential in optimizing large-scale problems. Nev-ertheless, they are expensive in terms of number of functionevaluations and this issue can be quite problematic whendealing with computationally expensive optimization problems.An effective approach to deal with such problems lies in theexploitation of surrogate models. The latter ones work as cheap-to-evaluate alternatives to the expensive function reducing thecomputational cost, while still providing improved designs. Thisprocess, called surrogate-assisted optimization, is very effective onsmall-dimensional problems but is not suitable to solve large-scaleproblems due to the curse of dimensionality. In this paper, a newalgorithm, taking benefit from cooperative coevolution and sur-rogate models, is introduced to efficiently solve high-dimensional,expensive and black-box problems. The proposed algorithm usesrecursive differential grouping to perform an accurate problemdecomposition. Experimental results are provided on a set of1000-dimensional problems and show promising results
KW - global optimization
KW - surrogate-assisted optimization
KW - Large-scale optimization
KW - high dimensional , expensive and black-box problems
KW - cooperative co-evolutionary algorithm
KW - differential grouping
KW - evolutionary algorithm
KW - global optimization
KW - surrogate-assisted optimization
KW - large-scale optimization
KW - high dimensional
KW - expensive and black-box problems
KW - cooperative co-evolutionary algorithm
KW - differential grouping
KW - evolutionary algorithm
UR - http://www.scopus.com/inward/record.url?scp=85071327186&partnerID=8YFLogxK
U2 - 10.1109/CEC.2019.8790114
DO - 10.1109/CEC.2019.8790114
M3 - Article
SP - 674
EP - 681
JO - 2019 IEEE Congress on Evolutionary Computation (CEC)
JF - 2019 IEEE Congress on Evolutionary Computation (CEC)
M1 - 18888677
ER -