Evolutionary Computation Group @ FER

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ECF - Evolutionary Computation Framework

ECF is a C++ framework intended for application of any type of evolutionary computation. Current features include:

• parameterless: genotype (individual structure) is the only mandatory parameter
• algorithms: steady state tournament, generational roulette-wheel, elimination, particle swarm optimization (PSO), differential evolution (DE), genetic annealing, artificial bee colony (ABC), clonal selection (CLONALG), immune optimization (optIA), evolution strategy, random search
• genetic algorithm genotypes (bitstring, binary encoded real values, floating point vectors, permutation vectors), genetic programming genotype (tree), analytic programming
• individuals may contain any genotypes in any number
• many examples: GP symbolic regression, GP artificial ant, function optimization, TSP, onemax, function optimization from CEC Real-Parameter Numerical Optimization
• parallel execution in many models (global paralel EA, distributed EA, hybrid parallel EA...) using MPI
• configurable environment: changing algorithm, genotypes and parameters without recompilation
• checkpointing and population restoring, various termination criteria, repeated runs, crx and mutation operators selection and usage rate, migration

More detailed feature list

Download

• current package (v 1.6, 2022/11/25) is available at Sourceforge: ECF_1.6.zip, ECF_1.6.tar.gz
• the latest development version can be found at https://github.com/djakobovic/ECF

Documentation

These files are also included in the package (./help directory).

•  Install: installation and how to build the examples
•  Tutorial: how to use ECF (the quickest way in)
•  Documentation at https://ecf.readthedocs.io/: it's all in the details
•  Reference: browse, download (zip) (doxygen produced)
•  a short FAQ (complement to the Tutorial)

Integration with COCO benchmark:

• example with COCO (BBoB) functions now includes BBoB experiment with any ECF algorithm, example results:

Support:

• Domagoj Jakobović (domagoj.jakobovic @ fer.hr)
• forum at Sourceforge

References

• Jakobović, Domagoj; Golub, Marin; Čupić, Marko. Asynchronous and implicitly parallel evolutionary computation models. // Soft computing. (2013)
• Picek, Stjepan; Jakobović, Domagoj; Golub, Marin. Evolving Cryptographically Sound Boolean Functions // Proceedings of the Genetic and Evolutionary Computation Conference 2013.
  Amsterdam : ACM, 2013. 191-192
• Picek, Stjepan; Jakobović, Domagoj; Golub, Marin. On the Recombination Operator in the Real-Coded Genetic Algorithms // Proceedings of the 2013 IEEE Congress on Evolutionary Computation.
  2013. 3103-3110
• Jakobović, Domagoj; Marasović, Kristina. Evolving priority scheduling heuristics with genetic programming. // Applied soft computing. 12 (2012) , 9; 2781-2789
• Picek, Stjepan; Golub, Marin; Jakobović, Domagoj. On the Analysis of Experimental Results in Evolutionary Computation // MIPRO 2012 International Convention. Opatija, 2012. 1245-1250 
• Picek, Stjepan; Golub, Marin; Jakobović, Domagoj. Evaluation of Crossover Operator Performance in Genetic Algorithms with Binary Representation. // Lecture Notes in Computer Science. 6840 (2011) ; 223-230
• Brajer, Iva; Jakobović, Domagoj. Automated Design of Combinatorial Logic Circuits // MIPRO 2012 International Convention.Opatija, 2012. 964-969
• Picek, Stjepan; Golub, Marin; Jakobović, Domagoj. Influence of the Crossover Operator in the Performance of the Hybrid Taguchi GA // Proceedings of the 2012 IEEE Congress on Evolutionary Computation / Xiaodong Li (ur.).
  Brisbane, 2012. 1480-1487

Contributors

(Alphabetical order): Hrvoje Ban, Vinko Bedek, Igor Bespaljko, Iva Brajer, Luka Donđivić, Luka Franov, Zvonimir Fras, Danko Komlen, Ivan Kokan, Luka Krizan, Maja Legac, Tomislav Novak, Lovro Paić-Antunović, Stjepan Picek, Dražen Popović, Ángel Ferreira-Santiago, Domagoj Stanković, Ivana Stokić, Mirjam Škarica

Project leader & support contact: Domagoj Jakobović (domagoj.jakobovic @ fer.hr)

Acknowledgements

ECF software uses the following components:

•  some features of boost C++ library
•  a simple but very useful XML parser

Additionally, this project was inspired by OpenBEAGLE, a comprehensive OO evolutionary framework.

Last update: 10.08.2022