All-implicants neural networks for efficient Boolean function representation

Federico Buffoni, Gabriele Gianini, Ernesto Damiani, Michael Granitzer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageBritish English
Title of host publicationProceedings - 2018 IEEE International Conference on Cognitive Computing, ICCC 2018 - Part of the 2018 IEEE World Congress on Services
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages82-86
Number of pages5
ISBN (Electronic)9781538672419
DOIs
StatePublished - 7 Sep 2018
Event2018 IEEE International Conference on Cognitive Computing, ICCC 2018 - San Francisco, United States
Duration: 2 Jul 20187 Jul 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Cognitive Computing, ICCC 2018 - Part of the 2018 IEEE World Congress on Services

Conference

Conference2018 IEEE International Conference on Cognitive Computing, ICCC 2018
Country/TerritoryUnited States
CitySan Francisco
Period2/07/187/07/18

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