A novel representation for boolean networks designed to enhance heritability and scalability

Daniel Ashlock, Gonzalo A. Ruz

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

Boolean networks are used to model gene regulatory networks at a relatively high level. Finding Boolean networks with particular properties requires a representation that permits efficient search. In this study a novel representation for Boolean networks is implemented that segments the functioning of the network model that defines the network into discrete pieces. This design is intended to facilitate crossover-based retention of functionality in the networks, i.e. to make properties in an evolving population more heritable. The representation is tested on three different fitness functions and, on one of them, compared to the direct evolution of the entries of a matrix. The fitness function used to compare the novel and direct matrix representation demonstrates substantial superiority of the novel representation. The other two functions demonstrate the effectiveness of the new representation at a diversity of tasks. The representation, while useful for Boolean networks, has a number of potential applications to other domains.

Idioma originalInglés
Título de la publicación alojada2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781467389884
DOI
EstadoPublicada - 4 oct. 2017
Evento2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017 - Manchester, Reino Unido
Duración: 23 ago. 201725 ago. 2017

Serie de la publicación

Nombre2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017

Conferencia

Conferencia2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
País/TerritorioReino Unido
CiudadManchester
Período23/08/1725/08/17

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