Self-Adaptive Mutation Operators for Genetic Neural Networks in Survival Analysis

University essay from Lunds universitet/Beräkningsbiologi och biologisk fysik; Lunds universitet/Institutionen för astronomi och teoretisk fysik

Abstract: Artificial neural networks (ANN) are used within the medical eld of survival analysis to rank patients according to their risk group. To evaluate how well the ranking was conducted, it is common to obtain the concordance index error (c-index). It has been shown that ANNs can be trained directly on the c-index with the use of genetic algorithms (GA). The GA evolution of an ANN is controlled by a set of operators, which in turn are governed by hyperparameters. These hyperparameters are usually static and set to generally accepted good values, or optimised through a grid search for each specific data set. In this article, adaptive and self-adaptive techniques are introduced to the hyperparameters that governs the mutation operators. It is shown that while training on the c-index, it is possible to simultaneously revise the mutation width. Furthermore, it is found that these techniques can be generalised to other aspects of mutation, and show promising results of the possibility of a self-adaptive genetic algorithm, independent of initialisation and data set.

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