TY - GEN
T1 - A novel representation for boolean networks designed to enhance heritability and scalability
AU - Ashlock, Daniel
AU - Ruz, Gonzalo A.
N1 - Funding Information:
Daniel Ashlock is with the Department of Mathematics and Statistics at the University of Guelph, in Guelph, Ontario, Canada, N1G 2W1, dashlock@uoguelph.ca Gonzalo A. Ruz is with the Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Santiago, Chile, and the Center of Applied Ecology and Sustainability (CAPES), Santiago, Chile, gonzalo.ruz@uai.cl The authors thank the University of Guelph, University Adolfo Ibáñez, Basal(CONICYT)-CMM, and the Canadian Natural Science and Engineering Research Council of Canada (NSERC) for supporting this work.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/4
Y1 - 2017/10/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85034667272&partnerID=8YFLogxK
U2 - 10.1109/CIBCB.2017.8058530
DO - 10.1109/CIBCB.2017.8058530
M3 - Conference contribution
AN - SCOPUS:85034667272
T3 - 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
BT - 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 August 2017 through 25 August 2017
ER -