GP fitness functions to evolve heuristics for planning

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{aler:2000:G,

• author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
• title = "GP fitness functions to evolve heuristics for planning",
• booktitle = "Evolutionary Methods for AI Planning",
• year = "2000",
• editor = "Martin Middendorf",
• pages = "189--195",
• address = "Las Vegas, Nevada, USA",
• month = "8 " # jul,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://scalab.uc3m.es/~dborrajo/papers/gecco00.ps.gz",
• abstract = "There are several ways of applying Genetic Programming (GP) to STRIPS-like planning in the literature. In this paper we emphasise the use of a new one, based on learning heuristics for planning. In particular, we focus on the design of fitness functions for this task. We explore two alternatives (black and white box fitness functions) and present some empirical results",
• size = "5 pages",
• notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}",

}

Genetic Programming entries for Ricardo Aler Mur Daniel Borrajo Pedro Isasi Vinuela

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