Comparing Evolutionary Algorithms on the Problem of Network Inference

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 di®erent 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 di®erent 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 = {},
}