In recent years several strategies for inferring gene regulatory networks from observed time series data of gene expression have been suggested based on Evolutionary Algorithms. But often only few problem instances are investigated and the proposed strategies are rarely compared to alternative strategies. In this paper we compare Evolution Strategies and Genetic Programming with respect to their performance on multiple problem instances with varying parameters. We show that single problem instances are not sufficient to prove the effectiveness of a given strategy and that the Genetic Programming approach is less prone to varying instances than the Evolution Strategy. © Sringer- Verlag Berlin Heidelberg 2004.
CITATION STYLE
Streichert, F., Planatscher, H., Spieth, C., Ulmer, H., & Zell, A. (2004). Comparing genetic programming and evolution strategies on inferring gene regulatory networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. https://doi.org/10.1007/978-3-540-24854-5_47
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