Julia implementation for paper "On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers", Gloria Pietropolli, Alessia Paoletti, Mauro Castelli, Luca Manzoni, 2022.
Geometric Semantic Genetic Programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.
We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.
Code runs with python 3.8.5 and Julia 1.4.1 on Ubuntu 20.04. To run the code with Gradient Descent optimizer, enter the following command:
julia gsgp.jl --problem --optimizer-- lr --p1 --p2To run the code with ADAM optimizer, enter the following command:
julia gsgp-adam.jl --problem --optimizer-- lr --p1 --p2where the inputs arguments stands for:
--problemis the benchmark considered--optimizeris the optimizer considered--lris the learning rate--p1is the number of steps of GSGP--p2is the number of steps of grdient-based optimizer
This run will return the fitness results for the 30 runs performed and save them in the results folder.
The considered benchmark problems are:
- human oral bioavaibility
- median lethal dose
- protein-plasma binding level
- yacht hydrodynamics
- concrete slump
- concrete compressive strenght
- airfoil self-noise
- parkinson
that are saved in the dataset folder.
To reproduce the box-plot of the paper, it is sufficient to run:
python3 boxplot.py To reproduce the plot of fitness w.r.t. the number of fitness evaluation, it is sufficient to run:
python3 rmse.py