Christian Spieth, Felix Streichert, Nora Speer, and Andreas Zell

**
Clustering Based Approach to Identify Solutions for the Inference of Regulatory Networks**

*
Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005)*

### Abstract

In this paper we address the problem of finding valid solutions for
the problem of inferring gene regulatory networks. Different
approaches to directly infer the dependencies of gene regulatory
networks by identifying parameters of mathematical models can be
found in literature. The problem of reconstructing regulatory
systems from experimental data is often multi-modal and thus
appropriate optimization strategies become necessary. Thus, we
propose to use a clustering based niching evolutionary algorithm to
maintain diversity in the optimization population to prevent
premature convergence and to raise the probability of finding the
global optimum by identifying multiple alternative networks. With
this set of alternatives, the identification of the true solution
has then to be addressed in a second post-processing step.

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**BibTeX**

@INPROCEEDINGS{spieth05clustering,

author = {Christian Spieth and Felix Streichert and Nora Speer and Andreas Zell},

title = {Clustering Based Approach to Identify Solutions for the Inference of Regulatory Networks},

booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005)},

year = {2005},

}