Genetic Programming for Kernel-based Learning with Co-evolving Subsets
  Selection
  Support Vector Machines (SVMs) are well-established Machine Learning (ML)
algorithms. They rely on the fact that i) linear learning can be formalized as
a well-posed optimization problem; ii) non-linear learning can be brought into
linear learning thanks to the kernel trick and the mapping of the initial
search space onto a high dimensional feature space. The kernel is designed by
the ML expert and it governs the efficiency of the SVM approach. In this paper,
a new approach for the automatic design of kernels by Genetic Programming,
called the Evolutionary Kernel Machine (EKM), is presented. EKM combines a
well-founded fitness function inspired from the margin criterion, and a
co-evolution framework ensuring the computational scalability of the approach.
Empirical validation on standard ML benchmark demonstrates that EKM is
competitive using state-of-the-art SVMs with tuned hyper-parameters.
