Julia implementation for paper "Combining Geometric Semantic GP with Gradient-descent Optimization", Gloria Pietropolli, Alessia Paoletti, Mauro Castelli, Luca Manzoni, 2021.
Geometric semantic genetic programming (GSGP) is a wellknown variant of genetic programming (GP) where recombination and mutation operators have a clear semantic effect. Both kind of operators have randomly selected parameters that are not optimized by the search process. In this paper we combine GSGP with a well-known gradientbased optimizer, Adam, in order to leverage the ability of GP to operate structural changes of the individuals with the ability of gradient-based methods to optimize the parameters of a given structure. Two methods, named HYB-GSGP and HeH-GSGP, are defined and compared with GSGP on a large set of regression problems, showing that the use of Adam can improve the performance on the test set. The idea of merging evolutionary computation and gradient-based optimization is a promising way of combining two methods with very different – and complementary – strengths.
Code runs with python 3.8.5 and Julia 1.4.1 on Ubuntu 20.04. To run the code, enter the following command:
julia gsgp_Adam.jl that will return the fitness results for the 100 runs performed for the following methods:
- GSGP
- HYB-GSGP
- HeH-GSGP
and save them in the results folder.
The script automatically perform the experiments for all the considered benchmark problems, that are:
- human oral bioavaibility
- median lethal dose
- protein-plasma binding level
- yacht hydrodynamics
- concrete slump
- concrete compressive strenght
- airfoil self-noise
that are saved in the dataset folder.
The code to reproduce the plot of the paper is contained in the folder plot, it is sufficient to run:
python3 boxplot.py For example, for the airfoil benchmark problem, the comparison of the fitness results obtained for training and testing set for different methods analyzed is:
