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GECCO
2006
Springer

Comparing evolutionary algorithms on the problem of network inference

10 years 3 months ago
Comparing evolutionary algorithms on the problem of network inference
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 different evolutionary 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 and a novel formulation, 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 different types of mutation and crossover operators to the inference problem for further comparative analysis. Categories and Subject Descriptors I.2 [Computing Methodologies]: ARTIFICIAL INTELLIGENCE--Miscellan...
Christian Spieth, Rene Worzischek, Felix Streicher
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where GECCO
Authors Christian Spieth, Rene Worzischek, Felix Streichert
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