Efficient global optimization for combinatorial problems

By Martin Zaefferer, Jörg Stork, Martina Friese, Andreas Fischbach, Boris Naujoks, Thomas Bartz-Beielstein. 2014

62 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jörg Stork
2. Martin Zaefferer
3. Jim E. Smith
4. Thomas Bartz-Beielstein
5. Agoston E. Eiben
6. David K. Gifford
7. Jonas W. Mueller
8. Tommi S. Jaakkola

In Computational Theory and Mathematics

1. Jörg Stork
2. Martin Zaefferer
3. Jim E. Smith
4. Thomas Bartz-Beielstein
5. Agoston E. Eiben
6. Jonas W. Mueller
7. Tommi S. Jaakkola
8. Xin Yao

In Management Science and Operations Research

1. Thomas Bartz-Beielstein
2. Martin Zaefferer
3. Jörg Stork
4. Agoston E. Eiben
5. David K. Gifford
6. Jonas W. Mueller
7. Jim E. Smith
8. Tommi S. Jaakkola

Citations in the corpus

• Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels (2019, methodology): “It is straightforward to extend kernel-based models to combinatorial search spaces [13,6].”
• Model-based methods for continuous and discrete global optimization (2017, background): “bin 1/3 ANN SA high ? 16 real world, pump positioning [77] bin 6 RBFN GA low dim(2) 10–25 NK-Landscape [78] -per 2 custom brute force high 28 6 real world: weld sequence [66] per 6 RBFN GA low 100 30–32 benchmark [79] per 6 Kriging GA low 100 12–32 benchmark [80] per 6 Kriging ACO low 100 1,000 50–1”
• From evolutionary computation to the evolution of things (2015, background): “A range of approaches, collectively known as surrogate models, are being developed that use computationally cheaper models in place of full fitness evaluations, and refine those models via occasional full evaluations of targeted individuals 72,73,74,75 .”
• Sequence to Better Sequence: Continuous Revision of Combinatorial Structures (2017, background): “Combinatorial structures are often optimized via complex search heuristics such as genetic programming (Zaefferer et al., 2014).”
• Linear Combination of Distance Measures for Surrogate Models in Genetic Programming (2018, unknown): “It is straightforward to extend kernel-based models to combinatorial search spaces [6,7].”
• Distance-based Kernels for Surrogate Model-based Neuroevolution (2018, methodology): “We employ the Kriging implementation of the R package CEGO [6,7].”