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» On the Brittleness of Evolutionary Algorithms
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BIBE
2007
IEEE
124views Bioinformatics» more  BIBE 2007»
15 years 4 months ago
Finding Cancer-Related Gene Combinations Using a Molecular Evolutionary Algorithm
—High-throughput data such as microarrays make it possible to investigate the molecular-level mechanism of cancer more efficiently. Computational methods boost the microarray ana...
Chan-Hoon Park, Soo-Jin Kim, Sun Kim, Dong-Yeon Ch...
CEC
2005
IEEE
15 years 3 months ago
A study on polynomial regression and Gaussian process global surrogate model in hierarchical surrogate-assisted evolutionary alg
This paper presents a study on Hierarchical Surrogate-Assisted Evolutionary Algorithm (HSAEA) using different global surrogate models for solving computationally expensive optimiza...
Zongzhao Zhou, Yew-Soon Ong, My Hanh Nguyen, Dudy ...
PPSN
2004
Springer
15 years 3 months ago
Searching Transcriptional Modules Using Evolutionary Algorithms
The mechanism of gene regulation has been studied intensely for decades. It is important to identify synergistic transcriptional motifs. Its search space is so large that an effici...
Je-Gun Joung, Sok June Oh, Byoung-Tak Zhang
DMIN
2006
126views Data Mining» more  DMIN 2006»
14 years 11 months ago
Comparison and Analysis of Mutation-based Evolutionary Algorithms for ANN Parameters Optimization
Mutation-based Evolutionary Algorithms, also known as Evolutionary Programming (EP) are commonly applied to Artificial Neural Networks (ANN) parameters optimization. This paper pre...
Kristina Davoian, Alexander Reichel, Wolfram-Manfr...
GECCO
2006
Springer
166views Optimization» more  GECCO 2006»
15 years 1 months ago
Comparing genetic robustness in generational vs. steady state evolutionary algorithms
Previous research has shown that evolutionary systems not only try to develop solutions that satisfy a fitness requirement, but indirectly attempt to develop genetically robust so...
Josh Jones, Terry Soule