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Found 5 papers, 2 papers with code
Stitching for Neuroevolution: Recombining Deep Neural Networks without Breaking Them
To correct for differing feature representations between these layers we employ stitching, which merges the networks by introducing new layers at crossover points.
Generating the Ground Truth: Synthetic Data for Label Noise Research
It allows for creating a noiseless dataset informed by real data, by either pre-specifying or learning a function and defining it as the ground truth function from which labels are generated.
The Impact of Asynchrony on Parallel Model-Based EAs
Thus, if an asynchronous parallel MBEA is also affected by an evaluation time bias, this could result in learned models to be less suited to solving the problem, reducing performance.
Solving Multi-Structured Problems by Introducing Linkage Kernels into GOMEA
This cannot be modelled sufficiently well when using linkage models that aim at capturing a single type of linkage structure, deteriorating the advantages brought by MBEAs.
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms
When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i. e., dependencies between variables, can be key.
