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Abstract

This dissertation describes, demonstrates, and analyzes a new search technique called stochastic iterated genetic hillclimbing (SIGH). SIGH performs function optimizations in high-dimensional, binary vector spaces. Although the technique can be characterized in abstract computational terms, it emerged from research into massively parallel connectionist learning machines, and it has a compact implementation in a connectionist architecture. The behavior generated by the machine displays elements of two existing search techniques—stochastic hillclimbing and genetic search.

Keywords

Search Strategy Global Maximum Strong Constraint Graph Partitioning Weak Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1987

Authors and Affiliations

  • David H. Ackley
    • 1
  1. 1.Carnegie Mellon UniversityUSA

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