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README.md

SGE: Structured Grammatical Evolution

(This is the original version of SGE. We have now a new version, which dynamically adjusts the size of the genotype. Please check: https://github.com/nunolourenco/dsge)

Structured Grammatical Evolution (SGE) is a recent Grammatical Evolution (GE) variant that aims at addressing some of its locality and redundancy issues. The SGE distinctive feature is having a one-to-one correspondence between genes and non-terminals of the grammar being used. If you use this code, a reference to the following work would be greatly appreciated:

@article{Lourenco2016,
 title={Unveiling the properties of structured grammatical evolution},
  author={Louren{\c{c}}o, Nuno and Pereira, Francisco B and Costa, Ernesto},
  journal={Genetic Programming and Evolvable Machines},
  volume={17},
  number={3},
  pages={251--289},
  year={2016},
  publisher={Springer}
}

This project corresponds to the vanilla version of the SGE engine. We provide the implementations of some problems that we used to test the SGE. Extending it to your own needs should be fairly easy.

When running the framework a folder called dumps will be created together with an additional one that corresponds to the experience. Inside, there will be directories for each run. Each run folder contains snapshots of the population at a given generation, and a file called progress_report.csv, which is assembled by the end of the evolutionary run. By default we take snapshots of the population at the following generations: 0,25 and 50. This can be changed, together with all the numeric values in the configs folder.

Requirements

Currently this codebase only works with python 2.

Instalation

To run the default examples, you should be inside the src directory and then type:

python -m examples.[problem_name] [run]

If you use the PyCharm IDE you can use it to open the src folder, and them, for the run configurations do the following:

Additional info

Grant Dick implemented SGE in C, as a module of his framework: libgges: Grammar-Guided Evolutionary Search

If you prefer C instead of python, please refer to his work.

Support

Any questions, comments or suggestion should be directed to Nuno Lourenço (naml@dei.uc.pt)

Acknowledgments

I am gratefull to my advisors Francisco B. Pereira and Ernesto Costa for their guidance during my PhD. I am also gratefull to Filipe Assunção and Joaquim Ferrer for their help and comments on the development of this framework.

Works using SGE

Assunçao, F., Lourenço, N., Machado, P., & Ribeiro, B. (2017). Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach.

Assunçao, F., Lourenço, N., Machado, P., & Ribeiro, B. (2017). Automatic Generation of Neural Networks with Structured Grammatical Evolution. In 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE.

Lourenço, N., Ferrer, J., Pereira, F. B., & Costa, E. (2017, April). A Comparative Study of Different Grammar-Based Genetic Programming Approaches. In European Conference on Genetic Programming (pp. 311-325). Springer, Cham.

Medvet, E., Bartoli, A., & Talamini, J. (2017, April). Road Traffic Rules Synthesis Using Grammatical Evolution. In European Conference on the Applications of Evolutionary Computation (pp. 173-188). Springer, Cham.

Medvet, E. (2017, April). A Comparative Analysis of Dynamic Locality and Redundancy in Grammatical Evolution. In European Conference on Genetic Programming (pp. 326-342). Springer, Cham.

Lourenço, Nuno, Francisco B. Pereira, and Ernesto Costa. "Unveiling the properties of structured grammatical evolution." Genetic Programming and Evolvable Machines 17.3 (2016): 251-289.

Lourenço, N., Pereira, F. B., & Costa, E. (2015, October). SGE: a structured representation for grammatical evolution. In International Conference on Artificial Evolution (Evolution Artificielle) (pp. 136-148). Springer International Publishing.

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