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Related Experiment Videos

Network of evolutionary processors with splicing rules and permitting context.

Ashish Choudhary1, Kamala Krithivasan

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai 600036, India. ashish@meenakshi.cs.iitm.ernet.in

Bio Systems
|October 19, 2006
PubMed
Summary
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Networks of evolutionary processors with splicing rules and permitting context (NEPPS) can generate languages and perform computations. Networks of size two with random context filters match Turing machine power and solve NP-complete problems efficiently.

Area of Science:

  • Theoretical Computer Science
  • Computational Biology
  • Formal Languages

Background:

  • Evolutionary processors utilize splicing rules, a biologically inspired operation, for language generation and computation.
  • Permitting context, or specific symbol presence, is required before applying splicing rules.
  • Input and output filters are associated with each node in the network.

Purpose of the Study:

  • To analyze networks of evolutionary processors with splicing rules and permitting context (NEPPS) as computational and language-generating devices.
  • To investigate the computational power of these networks, particularly concerning Turing machines and NP-complete problems.

Main Methods:

  • Modeling computational systems using networks of evolutionary processors with splicing and permitting context.

Related Experiment Videos

  • Analyzing the effect of input/output filters, specifically random context conditions, on network computational power.
  • Demonstrating the application of these networks to solve NP-complete problems.

    • Main Results:

    • Networks of size two with random context filters exhibit the computational power of Turing machines.
    • These networks can solve NP-complete problems in linear time.
    • The inclusion of permitting context and specific filtering mechanisms enhances computational capabilities.

    Conclusions:

    • NEPPS provide a powerful framework for language generation and computation.
    • The computational power of NEPPS can be tailored through network size and filter design.
    • NEPPS offer an efficient approach for addressing complex computational challenges, including NP-complete problems.