About

This is a Linear Genetic Programming (LGP) implementation in Python and C++.

Requirements

• Python 3 with Numpy.
• Swig, to make the interface with the evaluation function, which is in C++. With Anaconda, it can be installed via:

$ conda install -c anaconda swig

How to Use

First, one needs to compile the C++ modules with Swig. For that, you need to replace the following in compile_swig.sh in the code directory by the location in your machine.

-I/home/leo/anaconda3/include/python3.7m

After that, you need to run the script:

$ ./compile_swig.sh

The parameters that can be configured are explained in parameters.py, and run_single.py shows how to run the LGP algorithm. You can either set the parameters in the parameters.py file, instantiate a Parameters object, and pass it as argument when instantiating an LGP object, or change each parameter dinamically after instatiating the Parameters object. An example of this last option is shown in run_parallel.py, that runs different algorithm configurations on different problems in parallel.

This repository includes some example problems in the directory datasets, but one can add more problems as long as the CSV files follow the same structure. It's also possible to add more non-terminal functions or fitness measures by changing the parameters class and the fitness evaluation code.

Log Files

Each run generates some log files, that are stored in the location defined in your Parameters object. These files are:

• bestEffSizes: Effective size of the best program found at the last generation.
• bestSizes: Absolute size of the best program found at the last generation.
• best_test_[MEASURE]: Fitness of the best program found at the last generation, in the testing set. [MEASURE] refers to the measures defined in the Parameters object.
• best_train_[MEASURE]: Fitness of the best program found at the last generation, in the training set.
• evaluations: Fitness evaluations needed to find the optimal solution. -1 if the solution was not found.
• instEvaluations: Number of effective instruction evaluations needed to find the optimal solution. -1 if the solution was not found.
• meanEffSizes: Mean effective size of individuals in the population at the last generation.
• meanSizes: Mean absolute size of individuals in the population at the last generation.
• mean_test_[MEASURE]: Mean fitness of individuals in the population at the last generation, in the testing set.
• mean_train_[MEASURE]: Mean fitness of individuals in the population at the last generation, in the training set.
• solutions: Best program found, shown as a sequence of instructions.

If more runs are performed using a same log directory, each row in the CSV files correspond to a run. It is also possible to remove, change, or add log files by editing the code in LGP.py. For example, one may want to have the fitness values for all generations instead of only for the last one.

Citation

This LGP implementation is from the following publication:

@article{SottoGraphGP2021,
  author    = {L{\'{e}}o Fran{\c{c}}oso Dal Piccol Sotto and Paul Kaufmann and Timothy Atkinson and Roman Kalkreuth and M{\'{a}}rcio Porto Basgalupp},
  title     = {Graph representations in genetic programming},
  journal   = {Genetic Programming and Evolvable Machines},
  volume    = {22},
  number    = {4},
  pages     = {607--636},
  year      = {2021},
  url       = {https://doi.org/10.1007/s10710-021-09413-9},
  doi       = {10.1007/s10710-021-09413-9}
}