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Learning to control the program evolution process with cultural algorithms

Zannoni1, Reynolds

  • 1Computer Science Department, Wayne State University, Detroit, MI 48202, USA. elz@apollo.hp.com

Evolutionary Computation
|July 1, 1997
PubMed
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Cultural algorithms with genetic programming (CAGP) enhance software development by extracting knowledge to guide search and reduce variability. This approach improves performance and solution complexity compared to traditional genetic programming (GP).

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Software Engineering

Background:

  • Traditional software engineering relies on modularity and structured programming to minimize variability.
  • Genetic programming (GP) uses heuristic search and simulated evolution in a bottom-up approach to program development.
  • A key challenge is extracting knowledge from GP to focus search and reduce product variability.

Purpose of the Study:

  • To investigate the automatic extraction of knowledge from the genetic programming process.
  • To develop a novel system, cultural algorithms with genetic programming (CAGP), to guide the search process.
  • To reduce product variability and improve the efficiency of software development resources.

Main Methods:

  • A two-level system combining a population of genetic programs with a knowledge repository (belief set).

Related Experiment Videos

  • Microevolution within the population identifies meaningful program characteristics.
  • Beliefs (constraints) are extracted and used to guide genetic operators and modulate program parameters.

    • Main Results:

    • CAGP demonstrated improved average performance compared to GP alone.
    • A significant reduction in the complexity of the generated solutions was observed.
    • The execution time of CAGP was comparable to that of standard GP.

    Conclusions:

    • CAGP effectively extracts and utilizes knowledge to focus the search process in genetic programming.
    • The system leads to more efficient software development with reduced solution complexity.
    • This approach offers a promising direction for enhancing evolutionary computation techniques.