A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming

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@Article{Hong:2018:ASC,

• author = "Libin Hong and John H. Drake and John R. Woodward and Ender Ozcan",
• title = "A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming",
• journal = "Applied Soft Computing",
• year = "2018",
• volume = "62",
• pages = "162--175",
• month = jan,
• keywords = "genetic algorithms, genetic programming, Evolutionary programming, Automatic design, Hyper-heuristics, Continuous optimisation",
• ISSN = "1568-4946",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494617306051",
• DOI = "doi:10.1016/j.asoc.2017.10.002",
• size = "14 pages",
• abstract = "Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy, or Levy probability distribution. In this paper, we use genetic programming to automatically generate mutation operators for an evolutionary programming system, testing the proposed approach over a set of function classes, which represent a source of functions. The empirical results over a set of benchmark function classes illustrate that genetic programming can evolve mutation operators which generalise well from the training set to the test set on each function class. The proposed method is able to outperform existing human designed mutation operators with statistical significance in most cases, with competitive results observed for the rest.",
• notes = "hyperheuristic. Supplement C. Also known as \cite{HONG2018162}",

}

Genetic Programming entries for Libin Hong John H Drake John R Woodward Ender Ozcan

Citations