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arxiv: nlin/0511018 · v2 · pith:C3QSYBFVnew · submitted 2005-11-09 · 🌊 nlin.AO · cond-mat.stat-mech· cs.CC· nlin.CG· physics.bio-ph· q-bio.MN· q-bio.QM

The Role of Redundancy in the Robustness of Random Boolean Networks

classification 🌊 nlin.AO cond-mat.stat-mechcs.CCnlin.CGphysics.bio-phq-bio.MNq-bio.QM
keywords landscapesredundancyfitnessrbnsrobustnessbooleanchangeconjecture
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Evolution depends on the possibility of successfully exploring fitness landscapes via mutation and recombination. With these search procedures, exploration is difficult in "rugged" fitness landscapes, where small mutations can drastically change functionalities in an organism. Random Boolean networks (RBNs), being general models, can be used to explore theories of how evolution can take place in rugged landscapes; or even change the landscapes. In this paper, we study the effect that redundant nodes have on the robustness of RBNs. Using computer simulations, we have found that the addition of redundant nodes to RBNs increases their robustness. We conjecture that redundancy is a way of "smoothening" fitness landscapes. Therefore, redundancy can facilitate evolutionary searches. However, too much redundancy could reduce the rate of adaptation of an evolutionary process. Our results also provide supporting evidence in favour of Kauffman's conjecture (Kauffman, 2000, p.195).

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