A scalable symbolic expression tree interpreter for the heuristiclab optimization framework
Pages 1141 - 1148
Abstract
In this paper we describe a novel implementation of the Interpreter class for the metaheuristic optimization framework HeuristicLab, comparing it with the three existing interpreters provided with the framework. The Interpreter class is an internal software component utilized by HeuristicLab for the evaluation of the symbolic expression trees on which its Genetic Programming (GP) implementation relies. The proposed implementation is based on the creation and compilation of a .NET Expression Tree. We also analyze the Interpreters' performance, evaluating the algorithm execution times on GP Symbolic Regression problems for different run settings. Our implementation results to be the fastest on all evaluations, with comparatively better performance the larger the run population size, dataset length and tree size are, increasing HeuristicLab's computational efficiency for large problem setups.
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Index Terms
- A scalable symbolic expression tree interpreter for the heuristiclab optimization framework
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July 2014
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Published: 12 July 2014
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