R.I.P.
👻
Ghosted
Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results
June 12, 2017 · Declared Dead · 🏛 arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Bingshui Da, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Zexuan Zhu, Chuan-Kang Ting, Ke Tang, Xin Yao
arXiv ID
1706.03470
Category
cs.NE: Neural & Evolutionary
Citations
132
Venue
arXiv.org
Last Checked
2 months ago
Abstract
In this report, we suggest nine test problems for multi-task single-objective optimization (MTSOO), each of which consists of two single-objective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTSOO research.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
📜 Similar Papers
In the same crypt — Neural & Evolutionary
R.I.P.
👻
Ghosted
Progressive Growing of GANs for Improved Quality, Stability, and Variation
R.I.P.
👻
Ghosted
Learning both Weights and Connections for Efficient Neural Networks
R.I.P.
👻
Ghosted
LSTM: A Search Space Odyssey
R.I.P.
👻
Ghosted
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
R.I.P.
👻
Ghosted
An Introduction to Convolutional Neural Networks
Died the same way — 👻 Ghosted
R.I.P.
👻
Ghosted
Language Models are Few-Shot Learners
R.I.P.
👻
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
👻
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
👻
Ghosted