Regularization Approach to Inductive Genetic Programming

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@Article{nikolaev:2001:TEC,

• author = "Nikolay Y. Nikolaev and Hitoshi Iba",
• title = "Regularization Approach to Inductive Genetic Programming",
• journal = "IEEE Transactions on Evolutionary Computing",
• year = "2001",
• volume = "54",
• number = "4",
• pages = "359--375",
• month = aug,
• keywords = "genetic algorithms, genetic programming, learning (artificial intelligence), tree data structures, tree searching, data mining, Kolmogorov-Gabor polynomials, inductive genetic programming, learning polynomials, multivariate polynomials, tree structures, statistical bias, tree nodes, data mining, regularization, time series prediction, STROGANOFF,local search",
• ISSN = "1089-778X",
• URL = "http://ieeexplore.ieee.org/iel5/4235/20398/00942530.pdf?isNumber=20398",
• DOI = "doi:10.1109/4235.942530",
• size = "17 pages",
• abstract = "This paper presents an approach to regularization of inductive genetic programming tuned for learning polynomials. The objective is to achieve optimal evolutionary performance when searching high-order multivariate polynomials represented as tree structures. We show how to improve the genetic programming of polynomials by balancing its statistical bias with its variance. Bias reduction is achieved by employing a set of basis polynomials in the tree nodes for better agreement with the examples. Since this often leads to over-fitting, such tendencies are counteracted by decreasing the variance through regularization of the fitness function. We demonstrate that this balance facilitates the search as well as enables discovery of parsimonious, accurate, and predictive polynomials. The experimental results given show that this regularization approach outperforms traditional genetic programming on benchmark data mining and practical time-series prediction tasks.",
• notes = "Recombinative Guidance, FDC, MDL, IGP, Mackey-Glass, ANN",

}

Genetic Programming entries for Nikolay Nikolaev Hitoshi Iba

Citations