KGAE-CUP

Pinned Repositories

TaylorGP
Taylor Genetic Programming for Symbolic Regression
Language:Python93
SRNet-GECCO
Exploring Hidden Semantics for Neural Networks with Symbolic Regression
Language:Python22
AB-GEP
Language:Java00
BSEM
00
GVAE-ABGEP
Embedding Grammar Variational Autoencoder and Adversarial Bandit into Gene Expression Programming for Symbolic Regression
00
KGAE-CUP.github.io
Laboratory code repository
Language:HTML00
MTaylor_PPSN
PPSN material
00
NSGA-II
This is a python implementation of NSGA-II algorithm. NSGA is a popular non-domination based genetic algorithm for multi-objective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified version, NSGA II was developed, which has a better sorting algorithm , incorporates elitism and no sharing parameter needs to be chosen a priori.
Language:Python00
srbench
A living benchmark framework for symbolic regression
Language:Jupyter Notebook00

KGAE-CUP's Repositories

KGAE-CUP/MTaylor_PPSN
PPSN material
KGAE-CUP/KGAE-CUP.github.io
Laboratory code repository
Language:HTML
KGAE-CUP/BSEM
KGAE-CUP/srbench
A living benchmark framework for symbolic regression
Language:Jupyter Notebook
KGAE-CUP/TaylorGP
Taylor Genetic Programming for Symbolic Regression
Language:Python93
KGAE-CUP/GVAE-ABGEP
Embedding Grammar Variational Autoencoder and Adversarial Bandit into Gene Expression Programming for Symbolic Regression
KGAE-CUP/SRNet-GECCO
Exploring Hidden Semantics for Neural Networks with Symbolic Regression
Language:Python22
KGAE-CUP/AB-GEP
Language:Java
KGAE-CUP/NSGA-II
This is a python implementation of NSGA-II algorithm. NSGA is a popular non-domination based genetic algorithm for multi-objective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified version, NSGA II was developed, which has a better sorting algorithm , incorporates elitism and no sharing parameter needs to be chosen a priori.