On averaging the best samples in evolutionary computation
April 24, 2020 ยท Declared Dead ยท + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Laurent Meunier, Yann Chevaleyre, Jeremy Rapin, Clรฉment W. Royer, Olivier Teytaud
arXiv ID
2004.11685
Category
cs.NE: Neural & Evolutionary
Cross-listed
cs.LG,
stat.ML
Citations
0
Last Checked
4 months ago
Abstract
Choosing the right selection rate is a long standing issue in evolutionary computation. In the continuous unconstrained case, we prove mathematically that a single parent $ฮผ=1$ leads to a sub-optimal simple regret in the case of the sphere function. We provide a theoretically-based selection rate $ฮผ/ฮป$ that leads to better progress rates. With our choice of selection rate, we get a provable regret of order $O(ฮป^{-1})$ which has to be compared with $O(ฮป^{-2/d})$ in the case where $ฮผ=1$. We complete our study with experiments to confirm our theoretical claims.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Neural & Evolutionary
๐ฎ
๐ฎ
The Ethereal
R.I.P.
๐ป
Ghosted
Deep Learning using Rectified Linear Units (ReLU)
R.I.P.
๐ป
Ghosted
Generative Adversarial Text to Image Synthesis
R.I.P.
๐ป
Ghosted
Regularized Evolution for Image Classifier Architecture Search
R.I.P.
๐ป
Ghosted
Temporal Ensembling for Semi-Supervised Learning
๐
๐
Old Age
Learning Structured Sparsity in Deep Neural Networks
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted