- author = "Mario Graff and Eric S. Tellez and Sabino Miranda-Jimenez and Hugo Jair Escalante",
- title = "{EvoDAG}: A semantic {Genetic} {Programming} {Python} library",
- booktitle = "2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)",
- year = "2016",
- editor = "Jaime Cerda Jacobo",
- address = "Ixtapa, Vicente Guerrero, Mexico",
- month = "9-11 " # nov,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, semantic genetic programming, auto-sklearn, SVM",
- isbn13 = "978-1-5090-3794-0",
-
URL = "
http://ieeexplore.ieee.org/document/7830633/",
-
DOI = "
doi:10.1109/ROPEC.2016.7830633",
- size = "6 pages",
- abstract = "Genetic Programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard real-world problems. Lately, there has been considerable interest in GP's community to develop semantic genetic operators, i.e., operators that work on the phenotype. In this contribution, we describe EvoDAG (Evolving Directed Acyclic Graph) which is a Python library that implements a steady-state semantic Genetic Programming with tournament selection using an extension of our previous crossover operators based on orthogonal projections in the phenotype space. To show the effectiveness of EvoDAG, it is compared against state-of-the-art classifiers on different benchmark problems, experimental results indicate that EvoDAG is very competitive.",
-
notes = "See \cite{Handley:1994:DAGpcp}
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http://2016.ropec.org/bank-payment-information/comite-revisor/
https://github.com/mgraffg/EvoDAG",
