Multiobjective techniques for the use of state in genetic programming applied to simulated car racing

Alexandras Agapitos, Julian Togelius, Simon M. Lucas

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Multi-objective optimisation is applied to encourage the effective use of state variables in car controlling programs evolved using Genetic Programming. Three different metrics for measuring the use of state within a program are introduced. Comparisons are performed among multi- and single-objective fitness functions with respect to learning speed and final fitness of evolved individuals, and attempts are made at understanding whether there is a trade-off between good performance and stateful controllers in this problem domain.

    Original languageEnglish (US)
    Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
    Pages1562-1569
    Number of pages8
    DOIs
    StatePublished - Dec 1 2007
    Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
    Duration: Sep 25 2007Sep 28 2007

    Publication series

    Name2007 IEEE Congress on Evolutionary Computation, CEC 2007

    Other

    Other2007 IEEE Congress on Evolutionary Computation, CEC 2007
    CountrySingapore
    Period9/25/079/28/07

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    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software
    • Theoretical Computer Science

    Cite this

    Agapitos, A., Togelius, J., & Lucas, S. M. (2007). Multiobjective techniques for the use of state in genetic programming applied to simulated car racing. In 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 1562-1569). [4424659] (2007 IEEE Congress on Evolutionary Computation, CEC 2007). https://doi.org/10.1109/CEC.2007.4424659