Evolving Specific Network Statistical Properties using a Gene Regulatory Network Model

International audienceThe generation of network topologies with specific, user-specified statistical properties is the aim of this paper. This is achieved through the use of an artifical Gene Regulatory Network Model, shown previously to be able to correctly seed the population of an evolutionary algorithm, with the aim of steering the evolution towards the desired topologies. This method had previously been shown to be able to evolve scale-free topologies; the results obtained in this paper reinforce the applicability of the method, showing that the evolution of small-world topologies is also possible

Evolving Specific Network Statistical Properties using a Gene Regulatory Network Model

International audienceThe generation of network topologies with specific, user-specified statistical properties is the aim of this paper. This is achieved through the use of an artifical Gene Regulatory Network Model, shown previously to be able to correctly seed the population of an evolutionary algorithm, with the aim of steering the evolution towards the desired topologies. This method had previously been shown to be able to evolve scale-free topologies; the results obtained in this paper reinforce the applicability of the method, showing that the evolution of small-world topologies is also possible

Evolving Specific Network Statistical Properties using a Gene Regulatory Network Model

International audienceThe generation of network topologies with specific, user-specified statistical properties is addressed using an Evolutionary Algorithm that is seeded by an Artificial Gene Regulatory Network Model. The work presented here extends previous work where the proposed approach was demonstrated to be able to evolve scale-free topologies. The present results reinforce the applicability of the proposed method, showing that the evolution of small-world topologies is also possible, but requires a carefully crafted fitness function