Abstract
We use a modified version of the CLONALG algorithm to perform exploratory data analysis. Since we wish to compare results from a number of methods, we only report on linear projections which have unique solutions. We incorporate a type of Gram Schmidt orthogonalisation [15] into the affinity maturation process to capture multiple components. We combine the new algorithm with reinforcement learning [17, 20] and with cross entropy maximization [13, 19]. Finally we combine several different non-standard adaptation methods using bagging and show that we get reliable convergence to accurate filters.
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Wu, Y., Fyfe, C. Exploratory data analysis with artificial immune systems. Evol. Intel. 1, 159–169 (2008). https://doi.org/10.1007/s12065-008-0012-x
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DOI: https://doi.org/10.1007/s12065-008-0012-x