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Improving search algorithms by using intelligent coordinates.

David Wolpert1, Kagan Tumer, Esfandiar Bandari

  • 1NASA, Ames Research Center, Moffett Field, California 94035, USA. dhw@email.arc.nasa.gov

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 5, 2004
PubMed
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This study introduces a novel distributed algorithm using machine learning agents in noncooperative games to enhance search algorithm performance. This approach significantly improves optimization tasks like bin packing and economic modeling.

Area of Science:

  • Distributed algorithms
  • Computational intelligence
  • Optimization

Background:

  • Distributed algorithms often face challenges in balancing global function maximization with individual agent self-interest.
  • Performance is influenced by exploration/exploitation strategies, game theory dynamics, and machine learning principles.

Purpose of the Study:

  • To develop a novel distributed algorithm that enhances global function maximization.
  • To integrate machine learning and game theory into the exploration phase of search algorithms.

Main Methods:

  • Agents act as self-interested players in a noncooperative game, controlling search space coordinates.
  • The exploration stage of a search algorithm is modified to incorporate these machine learning-based players.
  • Simulated annealing (SA) is used as a baseline for performance comparison.

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Main Results:

  • The modified algorithm demonstrated up to an order of magnitude improvement over standard SA for bin packing.
  • Significant performance gains were observed in a model of an economic process over a network.
  • The experiments revealed emergent small-world phenomena.

Conclusions:

  • Integrating game theory and machine learning into distributed algorithms offers substantial performance benefits.
  • This approach effectively addresses the exploration/exploitation dilemma in complex optimization problems.
  • The findings suggest new avenues for designing efficient distributed computational systems.