Evolving crossover operators for function optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{eurogp06:DiosanOltean,

• author = "Laura Dio\c{s}an and Mihai Oltean",
• title = "Evolving crossover operators for function optimization",
• editor = "Pierre Collet and Marco Tomassini and Marc Ebner and Steven Gustafson and Anik\'o Ek\'art",
• booktitle = "Proceedings of the 9th European Conference on Genetic Programming",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "3905",
• year = "2006",
• address = "Budapest, Hungary",
• month = "10 - 12 " # apr,
• organisation = "EvoNet",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-33143-3",
• pages = "97--108",
• DOI = "doi:10.1007/11729976_9",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• abstract = "A new model for evolving crossover operators for evolutionary function optimisation is proposed in this paper. The model is a hybrid technique that combines a Genetic Programming (GP) algorithm and a Genetic Algorithm (GA). Each GP chromosome is a tree encoding a crossover operator used for function optimization. The evolved crossover is embedded into a standard Genetic Algorithm which is used for solving a particular problem. Several crossover operators for function optimisation are evolved using the considered model. The evolved crossover operators are compared to the human-designed convex crossover. Numerical experiments show that the evolved crossover operators perform similarly or sometimes even better than standard approaches for several well-known benchmarking problems.",
• notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in conjunction with EvoCOP2006 and EvoWorkshops2006",

}

Genetic Programming entries for Laura Diosan Mihai Oltean

Citations