Genetic Programming and Model Checking: Synthesizing New Mutual Exclusion Algorithms

  • Gal Katz
  • Doron Peled
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5311)


Recently, genetic programming and model checking were combined for synthesizing algorithms that satisfy a given specification [7,6]. In particular, we demonstrated this approach by developing a tool that was able to rediscover the classical mutual exclusion algorithms [7] with two or three global bits. In this paper we extend the capabilities of the model checking-based genetic programming and the tool built to experiment with this approach. In particular, we add qualitative requirements involving locality of variables and checks, which are typical of realistic mutual exclusion algorithms. The genetic process mimics the actual development of mutual exclusion algorithms, by starting with an existing correct solution, which does not satisfy some performance requirements, and converging into a solution that satisfies these requirements. We demonstrate this by presenting some nontrivial new mutual exclusion algorithms, discovered with our tool.


Model Check Genetic Programming Critical Section Mutual Exclusion Linear Temporal Logic 
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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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gal Katz
    • 1
  • Doron Peled
    • 1
  1. 1.Department of Computer ScienceBar Ilan UniversityRamat GanIsrael

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