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Long-Term Evolution Experiment with Genetic Programming.

William B Langdon1, Wolfgang Banzhaf2

  • 1University College London, Department of Computer Science. W.Langdon@cs.ucl.ac.uk.

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Summary
This summary is machine-generated.

Genetic programming (GP) evolves complex programs, but deep expression trees limit learning. Open complexity and efficient computation are key for unbounded evolution and innovation in genetic programming.

Keywords:
Genetic programmingextended unlimited evolutioninformation theory limit on complexitylong-term evolution experiment (LTEE)open complexityspeed-up technique

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Area of Science:

  • Computational intelligence
  • Evolutionary computation
  • Artificial intelligence

Background:

  • Floating-point genetic programming (GP) evolves binary trees.
  • Long-term evolution experiments face challenges with complexity and computational demands.

Purpose of the Study:

  • To investigate the limits of innovation in GP by evolving Sextic polynomial populations.
  • To explore the concept of 'open complexity' as an alternative to deep, nested program structures.
  • To develop efficient computational methods for supporting large-scale GP evolution.

Main Methods:

  • Evolving floating-point Sextic polynomial populations of GP binary trees for up to one million generations.
  • Utilizing incremental fitness evaluation for efficient computation.
  • Employing Single Instruction, Multiple Data (SIMD) parallel AVX 512-bit instructions and 16 threads for high-performance computing.
  • Achieving computational speeds of 1.1 tera GPops (trillion GP operations per second) on an Intel Xeon Gold 6136 CPU.

Main Results:

  • Observed continued innovation in GP populations over extended evolutionary periods.
  • Identified tree depth as a limiting factor for learning and evolvability due to information dispersal.
  • Demonstrated the creation of programs exceeding 2 billion instructions (depth 20,000) via crossover.
  • Achieved significant computational performance enabling large-scale evolution.

Conclusions:

  • Deep expression trees in GP hinder evolvability and adaptation by dispersing information.
  • Advocating for 'open complexity' rather than extreme nesting to facilitate unbounded evolution.
  • High-performance computing, including SIMD and multi-threading, is crucial for supporting long-term, large-scale GP experiments.