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Practical performance models of algorithms in evolutionary program induction and other domains

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Title: Practical performance models of algorithms in evolutionary program induction and other domains
Authors: Graff, Mario mgraff@essex.ac.uk, Poli, Riccardo1 rpoli@essex.ac.uk
Source: Artificial Intelligence. Oct2010, Vol. 174 Issue 15, p1254-1276. 23p.
Subjects: Algorithms, Computer networks, Genetic programming, Genetic algorithms, Regression analysis, Boolean algebra, Evolutionary computation, Performance evaluation
Abstract: Abstract: Evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of both exact and approximate models for evolutionary program induction algorithms. However, these models are often criticised for being only applicable to simplistic problems or algorithms with unrealistic parameters. In this paper, we start rectifying this situation in relation to what matters the most to practitioners and users of program induction systems: performance. That is, we introduce a simple and practical model for the performance of program-induction algorithms. To test our approach, we consider two important classes of problems — symbolic regression and Boolean function induction — and we model different versions of genetic programming, gene expression programming and stochastic iterated hill climbing in program space. We illustrate the generality of our technique by also accurately modelling the performance of a training algorithm for artificial neural networks and two heuristics for the off-line bin packing problem. We show that our models, besides performing accurate predictions, can help in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. We illustrate this via the automatic construction of a taxonomy for the stochastic program-induction algorithms considered in this study. The taxonomy reveals important features of these algorithms from the performance point of view, which are not detected by ordinary experimentation. [ABSTRACT FROM AUTHOR]
Copyright of Artificial Intelligence is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Practical performance models of algorithms in evolutionary program induction and other domains
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  Data: <searchLink fieldCode="AR" term="%22Graff%2C+Mario%22">Graff, Mario</searchLink><i> mgraff@essex.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Poli%2C+Riccardo%22">Poli, Riccardo</searchLink><relatesTo>1</relatesTo><i> rpoli@essex.ac.uk</i>
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  Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink>. Oct2010, Vol. 174 Issue 15, p1254-1276. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+networks%22">Computer networks</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+programming%22">Genetic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Boolean+algebra%22">Boolean algebra</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+computation%22">Evolutionary computation</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+evaluation%22">Performance evaluation</searchLink>
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  Data: Abstract: Evolutionary computation techniques have seen a considerable popularity as problem solving and optimisation tools in recent years. Theoreticians have developed a variety of both exact and approximate models for evolutionary program induction algorithms. However, these models are often criticised for being only applicable to simplistic problems or algorithms with unrealistic parameters. In this paper, we start rectifying this situation in relation to what matters the most to practitioners and users of program induction systems: performance. That is, we introduce a simple and practical model for the performance of program-induction algorithms. To test our approach, we consider two important classes of problems — symbolic regression and Boolean function induction — and we model different versions of genetic programming, gene expression programming and stochastic iterated hill climbing in program space. We illustrate the generality of our technique by also accurately modelling the performance of a training algorithm for artificial neural networks and two heuristics for the off-line bin packing problem. We show that our models, besides performing accurate predictions, can help in the analysis and comparison of different algorithms and/or algorithms with different parameters setting. We illustrate this via the automatic construction of a taxonomy for the stochastic program-induction algorithms considered in this study. The taxonomy reveals important features of these algorithms from the performance point of view, which are not detected by ordinary experimentation. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Artificial Intelligence is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.artint.2010.07.005
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      – Code: eng
        Text: English
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        PageCount: 23
        StartPage: 1254
    Subjects:
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Computer networks
        Type: general
      – SubjectFull: Genetic programming
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Regression analysis
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      – SubjectFull: Boolean algebra
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      – SubjectFull: Evolutionary computation
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      – SubjectFull: Performance evaluation
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      – TitleFull: Practical performance models of algorithms in evolutionary program induction and other domains
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              M: 10
              Text: Oct2010
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              Y: 2010
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