All-Implicants Neural Networks for Efficient Boolean Function Representation

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@InProceedings{Buffoni:2018:ICCC,

• author = "Federico Buffoni and Gabriele Gianini and Ernesto Damiani and Michael Granitzer",
• booktitle = "2018 IEEE International Conference on Cognitive Computing (ICCC)",
• title = "All-Implicants Neural Networks for Efficient Boolean Function Representation",
• year = "2018",
• pages = "82--86",
• abstract = "Boolean classifiers can be evolved by means of genetic algorithms. This can be done within an intercommunicating island system, of evolutionary niches, undergoing cycles that alternate long periods of isolation to short periods of information exchange. In these settings, the efficiency of the communication is a key requirement. In the present work, we address this requirement by providing a technique for efficiently representing and transmitting differential encodings of Boolean functions. We introduce a new class of Boolean Neural Networks (BNN), the all-implicants BNN, and show that this representation supports efficient update communication, better than the classical representation, based on truth tables.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ICCC.2018.00019",
• month = jul,
• notes = "Also known as \cite{8457700}",

}

Genetic Programming entries for Federico Buffoni Gabriele Gianini Ernesto Damiani Michael Granitzer

Citations