An evolutionary algorithm for LTS-Regression: A comparative study
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Mutation: replace one of the p points by one of the n− p other points. Move: parallel shift of the estimated hyperplane, such that it hits a random one of the n-p remaining points. π is controlled with the parameter percentagemove PER. Simulated Annealing ANN for not getting trapped in a local optimum. Intercept adjustment ADJ possible for reducing the LTS–value further. Further parameters: waitforimprovement WFI, which is controlling nW, total number of generations GEN and total number of iterations ITE. 4.) Designed Experiments for comparison of algorithms
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