C. Spieth, F. Streichert, J. Supper, N. Speer, and A. Zell

**
Feedback Memetic Algorithms for Modeling Gene Regulatory Networks**

*
Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005)*

### Abstract

In this paper we address the problem of finding
gene regulatory networks from experimental DNA microarray
data. We focus on the evaluation of the performance of memetic
algorithms on the inference problem. These algorithms are used
to evolve an underlying quantitative mathematical model. The dynamics
of the regulatory system are modeled with two commonly
used approaches, namely linear weight matrices and S-systems.
Due to the complexity of the inference problem, some researchers
suggested evolutionary algorithms for this purpose. We introduce
memetic enhancements to this optimization process to infer the
parameters of sparsely connected nonlinear systems from the
observed data. Due to the limited number of available data, the
inferring problem is underdetermined and ambiguous. Further
on, the problem often is multimodal and therefore appropriate
optimization strategies become necessary. We propose a memetic
method, which separates the overall inference problem into two
subproblems to find the correct network: first, the search for a
valid topology, and secondly, the optimization of the parameters
of the mathematical model. The performance and the properties
of the proposed methods are evaluated and compared to standard
algorithms found in the literature.

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**BibTeX**

@INPROCEEDINGS{spieth05feedback,

author = {C. Spieth, F. Streichert, J. Supper, N. Speer, and A. Zell},

title = {Feedback Memetic Algorithms for Modeling Gene Regulatory Networks},

booktitle = {Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005)},

year = {2005},

pages = {61-67},

}