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A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement

Shingo Mabu1, Kotaro Hirasawa, Jinglu Hu

  • 1Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7 Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan. mabu@asagi.waseda.jp

Evolutionary Computation
|August 21, 2007
PubMed
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Genetic Network Programming (GNP) is a novel graph-based evolutionary algorithm designed for dynamic environments. This approach, enhanced with reinforcement learning (GNPRL), shows advantages in agent behavior determination compared to traditional methods.

Area of Science:

  • Artificial Intelligence
  • Evolutionary Computation
  • Machine Learning

Background:

  • Dynamic environments pose significant challenges for traditional algorithms.
  • Graph-based structures offer unique advantages in representing complex relationships and enabling efficient computation.
  • Existing evolutionary algorithms may struggle with adaptability in rapidly changing scenarios.

Purpose of the Study:

  • To introduce Genetic Network Programming (GNP), a novel graph-based evolutionary algorithm.
  • To enhance GNP's performance in dynamic environments by integrating reinforcement learning (GNPRL).
  • To evaluate the effectiveness of GNP and GNPRL in solving agent behavior determination problems.

Main Methods:

  • GNP utilizes a network structure of nodes and directed links for program representation.

Related Experiment Videos

  • Key features include node reusability for compactness and implicit memory through node transitions.
  • The extended GNPRL algorithm combines evolutionary processes with reinforcement learning for improved adaptability.

    • Main Results:

    • GNP demonstrated efficient and effective performance in dynamic environments.
    • The application to agent behavior determination in Tileworld showed competitive results.
    • GNPRL exhibited advantages over conventional methods, particularly in complex, evolving scenarios.

    Conclusions:

    • GNP's graph structure provides a powerful framework for evolutionary computation in dynamic settings.
    • The integration of reinforcement learning significantly boosts the performance of GNP in adaptive tasks.
    • GNP and GNPRL represent a promising advancement for artificial intelligence in dynamic environments.