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Learnable Embeddings of Program Spaces

  • Krzysztof Krawiec
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)

Abstract

We consider a class of adaptive, globally-operating, semantic-based embeddings of programs into discrete multidimensional spaces termed prespaces. In the proposed formulation, the original space of programs and its prespace are bound with a learnable mapping, where the process of learning is aimed at improving the overall locality of the new representation with respect to program semantics. To learn the mapping, which is formally a permutation of program locations in the prespace, we propose two algorithms: simple greedy heuristics and an evolutionary algorithm. To guide the learning process, we use a new definition of semantic locality. In an experimental illustration concerning four symbolic regression domains, we demonstrate that an evolutionary algorithm is able to improve the embedding designed by means of greedy search, and that the learned prespaces usually offer better search performance than the original program space.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Krzysztof Krawiec
    • 1
  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznańPoland

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