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

**
Comparing Evolutionary Algorithms on the Problem of Network Inference**

*
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006)*

### Abstract

In this paper, we address the problem of ¯nding gene regula-
tory networks from experimental DNA microarray data. We
focus on the evaluation of the performance of diŽerent evo-
lutionary algorithms on the inference problem. These algo-
rithms are used to evolve an underlying quantitative math-
ematical model. The dynamics of the regulatory system
are modeled with two commonly used approaches, namely
linear weight matrices and S-systems and a novel formu-
lation, namely H-systems. Due to the complexity of the
inference problem, some researchers suggested evolutionary
algorithms for this purpose. However, in many publications
only one algorithm is used without any comparison to other
optimization methods. Thus, we introduce a framework to
systematically apply evolutionary algorithms and diŽerent
types of mutation and crossover operators to the inference
problem for further comparative analysis.

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**BibTeX**

@INPROCEEDINGS{spieth06eas,

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

title = {Comparing Evolutionary Algorithms on the Problem of Network Inference},

booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006)},

year = {2006},

pages = {},

}