The Geometry of Tartarus Fitness Cases

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

@InProceedings{Ashlock3:2008:cec,

• author = "Daniel Ashlock and Elizabeth Warner",
• title = "The Geometry of Tartarus Fitness Cases",
• booktitle = "2008 IEEE World Congress on Computational Intelligence",
• year = "2008",
• editor = "Jun Wang",
• pages = "1309--1316",
• address = "Hong Kong",
• month = "1-6 " # jun,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• isbn13 = "978-1-4244-1823-7",
• file = "EC0339.pdf",
• DOI = "doi:10.1109/CEC.2008.4630965",
• size = "8 pages",
• abstract = "Tartarus is a standard AI task for grid robots in which boxes must be moved to the walls of a virtual world. There are 320320 fitness cases for the standard Tartarus task of which 297040 are valid according to the original statement of the problem. This paper studies different schemes for allocating fitness trials for Tartarus using an agent-based metric on the fitness cases to aid in the design process. This agent-based metric is a tool that permits exploration of the geometry of the space of fitness cases. The information gained from this exploration demonstrates why a scheme designed to yield a superior set of training cases in fact yielded an inferior one. The information gained also suggests a new scheme for allocating fitness trials that decreases the number of trials required to achieve a given fitness of the best agent. This scheme achieves similar fitness to a standard evolutionary algorithm using fewer fitness cases. The space of fitness cases for Tartarus is found, relative to the agent-based metric, to form a hollow sphere with a nonuniform distribution of the fitness cases within the space. The tools developed in this study include a generalisable technique for placing an agent-based metric space structure on the fitness cases of any problem that has multiple fitness cases. This metric space structure can be used to better understand the distribution of fitness cases and so design more effective evolutionary algorithms.",
• keywords = "genetic algorithms, genetic programming",
• notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.",

}

Genetic Programming entries for Daniel Ashlock Elizabeth Warner

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