Theory-Laden Design of Mutation-Based Geometric Semantic Genetic Programming for Learning Classification Trees

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Mambrini:2013:CEC,

• article_id = "1697",
• author = "Andrea Mambrini and Luca Manzoni and Alberto Moraglio",
• title = "Theory-Laden Design of Mutation-Based Geometric Semantic Genetic Programming for Learning Classification Trees",
• booktitle = "2013 IEEE Conference on Evolutionary Computation",
• volume = "1",
• year = "2013",
• month = jun # " 20-23",
• editor = "Luis Gerardo {de la Fraga}",
• pages = "416--423",
• address = "Cancun, Mexico",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4799-0453-2",
• DOI = "doi:10.1109/CEC.2013.6557599",
• size = "8 pages",
• abstract = "Geometric Semantic Genetic Programming (GSGP) is a recently introduced framework to design domain-specific search operators for Genetic Programming (GP) to search directly the semantic space of functions. The fitness landscape seen by GSGP is always - for any domain and for any problem - unimodal with a constant slope by construction. This makes the search for the optimum much easier than for traditional GP, and it opens the way to analyse theoretically in a easy manner the optimisation time of GSGP in a general setting. We design and analyse a mutation-based GSGP for the class of all classification tree learning problems, which is a classic GP application domain.",
• notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and the IET.",

}

Genetic Programming entries for Andrea Mambrini Luca Manzoni Alberto Moraglio

Citations