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Related Experiment Videos

Ideal evaluation from coevolution.

Edwin D de Jong1, Jordan B Pollack

  • 1DEMO Lab, Volen National Center for Complex Systems, Brandeis University MS018, 415 South Street, Waltham MA 02454-9110, USA. dejong@cs.uu.nl

Evolutionary Computation
|May 26, 2004
PubMed
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This study introduces a Complete Evaluation Set for coevolutionary algorithms, enabling ideal performance evaluation. The DELPHI algorithm uses this set to achieve sustained progress, overcoming overspecialization in complex problems.

Area of Science:

  • Artificial Intelligence
  • Evolutionary Computation
  • Machine Learning

Background:

  • Performance evaluation in complex problems often relies on tests, but evaluating all possible tests is infeasible.
  • Approximations in evaluation can introduce human bias, and current coevolutionary methods often lead to inaccurate evaluations.
  • Accurate evaluation is crucial for effective search processes in evolutionary algorithms.

Purpose of the Study:

  • To develop a principled approach for ideal evaluation in coevolutionary systems.
  • To introduce a method for determining a Complete Evaluation Set that ensures ideal evaluation.
  • To present an algorithm, DELPHI, that leverages this set for improved performance.

Main Methods:

  • Determining a Complete Evaluation Set based on Evolutionary Multi-Objective Optimization principles.

Related Experiment Videos

  • Developing and implementing the DELPHI algorithm to utilize the Complete Evaluation Set.
  • Testing the DELPHI algorithm on problems with subsets of underlying objectives, comparing it with existing methods.

    • Main Results:

    • The Complete Evaluation Set is of manageable size and allows accurate progress measurement.
    • DELPHI and a variant achieved sustained progress across all underlying objectives, unlike comparison methods that resulted in overspecialization.
    • Demonstrated that ideal evaluation can be approximated by practical algorithms, even with unknown problem objectives.

    Conclusions:

    • A principled approach to ideal evaluation in coevolution is established through the Complete Evaluation Set.
    • The DELPHI algorithm provides a practical solution for accurate evaluation in test-based problems.
    • This work shows that accurate evaluation and sustained progress are achievable even when underlying objectives are unknown.