Implement some metaheuristics and cost functions

Last update: Mar 23, 2022

Related tags

Deep Learning metaheuristics

Overview

Metaheuristics

This repot implement some metaheuristics and cost functions.

Metaheuristics

JAYA

Implement Jaya optimizer without constraints.

Cost functions

Himmelblau's function Himmelblau.png

References

[1]R. Venkata Rao, Jaya: An Advanced Optimization Algorithm and its Engineering Applications. Cham: Springer International Publishing, 2019. doi: 10.1007/978-3-319-78922-4.

[2]Wikipedia contributors, « Test functions for optimization », Wikipedia, The Free Encyclopedia., juin 27, 2021. https://en.wikipedia.org/w/index.php?title=Test_functions_for_optimization&oldid=1030693803 (consulté le oct. 01, 2021).

Owner

Adri1G

PhD student LS2N & Centrale Nantes. Interested in smart grids, embedded systems, electronics, Eurobot/ Coupe de Robotique and beautiful algorithms.

GitHub Repository

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Implement some metaheuristics and cost functions

Related tags

Overview

Metaheuristics

Metaheuristics

JAYA

Cost functions

References

Owner

Adri1G

The official implementation of the CVPR 2021 paper FAPIS: a Few-shot Anchor-free Part-based Instance Segmenter

Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

Fast Soft Color Segmentation

A python library for self-supervised learning on images.

Pytorch implemenation of Stochastic Multi-Label Image-to-image Translation (SMIT)

Super Pix Adv - Offical implemention of Robust Superpixel-Guided Attentional Adversarial Attack (CVPR2020)

TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

An Artificial Intelligence trying to drive a car by itself on a user created map

Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

CVPR 2021: "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"

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TorchMD-Net provides state-of-the-art graph neural networks and equivariant transformer neural networks potentials for learning molecular potentials

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