We present and discuss the results of an experimental analysis in the design of Boolean networks by means of genetic algorithms. A population of networks is evolved with the aim of nding a network such that the attractor it reaches is of required length l. In general, any target can be dened, provided that it is possible to model the task as an optimisation problem over the space of networks. We experiment with dierent initial conditions for the networks, namely in ordered, chaotic and critical regions, and also with dierent target length values. Results show that all kinds of initial networks can attain the desired goal, but with dierent success ratios: initial populations composed of critical or chaotic networks are more likely to reach the target. Moreover, the evolu tion starting from critical networks achieves the best overall performance. This study is the rst step toward the use of search algorithms as tools for automatically design Boolean networks with required properties.

Boolean Networks Design by Genetic Algorithms / A.Roli; C.Arcaroli; M.Lazzarini; S.Benedettini. - ELETTRONICO. - (2009), pp. 1-12. (Intervento presentato al convegno First workshop on Complexity, evolution and emergent intelligence tenutosi a Reggio Emilia (Italia) nel December 12, 2009).

Boolean Networks Design by Genetic Algorithms

ROLI, ANDREA;BENEDETTINI, STEFANO
2009

Abstract

We present and discuss the results of an experimental analysis in the design of Boolean networks by means of genetic algorithms. A population of networks is evolved with the aim of nding a network such that the attractor it reaches is of required length l. In general, any target can be dened, provided that it is possible to model the task as an optimisation problem over the space of networks. We experiment with dierent initial conditions for the networks, namely in ordered, chaotic and critical regions, and also with dierent target length values. Results show that all kinds of initial networks can attain the desired goal, but with dierent success ratios: initial populations composed of critical or chaotic networks are more likely to reach the target. Moreover, the evolu tion starting from critical networks achieves the best overall performance. This study is the rst step toward the use of search algorithms as tools for automatically design Boolean networks with required properties.
2009
Proceedings of the first workshop on Complexity, evolution and emergent intelligence
1
12
Boolean Networks Design by Genetic Algorithms / A.Roli; C.Arcaroli; M.Lazzarini; S.Benedettini. - ELETTRONICO. - (2009), pp. 1-12. (Intervento presentato al convegno First workshop on Complexity, evolution and emergent intelligence tenutosi a Reggio Emilia (Italia) nel December 12, 2009).
A.Roli; C.Arcaroli; M.Lazzarini; S.Benedettini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/85772
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