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Memetic algorithms are search methods coupling population-based evolution and individual learning, and are attracting growing attention in the recent two decades. As computational cost is usually an essential factor of concern, there has been increasing research on optimizing and adapting the frequency and budget of individual learning, in order to achieve efficient search. However, most of current...
We present a memetic algorithm for evolving the structure of biomolecular interactions and inferring the effective kinetic parameters from the time-series data of gene expression using the decoupled S-system formalism. We propose an Information Criteria-based fitness evaluation for gene network model selection instead of the conventional Mean Squared Error (MSE)-based fitness evaluation. A hill-climbing...
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