Tikhonov Regularization as a Complexity Measure in Multiobjective Genetic Programming

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@Article{Ni:2014:ieeeTEC,

• author = "Ji Ni and Peter Rockett",
• title = "Tikhonov Regularization as a Complexity Measure in Multiobjective Genetic Programming",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2015",
• volume = "19",
• number = "2",
• pages = "157--166",
• month = apr,
• keywords = "genetic algorithms, genetic programming, Tikhonov regularisation, Complexity measure, Pareto dominance",
• ISSN = "1089-778X",
• URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6746085",
• DOI = "doi:10.1109/TEVC.2014.2306994",
• size = "10 pages",
• abstract = "We propose using Tikhonov regularisation in conjunction with node count as a general complexity measure in multiobjective genetic programming. We demonstrate that employing this general complexity yields mean squared test error measures over a range of regression problems which are typically superior to those from conventional node count (but never statistically worse). We also analyse the reason why our new method outperforms the conventional complexity measure and conclude that it forms a decision mechanism which balances both syntactic and semantic information.",
• notes = "also known as \cite{6746085}",

}

Genetic Programming entries for Ji Ni Peter I Rockett

Citations