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Related Experiment Videos

A monotonic archive for pareto-coevolution.

Edwin D de Jong1

  • 1Institute of Information and Computing Sciences, Utrecht University, PO Box 80.089, 3508 TB Utrecht, The Netherlands. dejong@cs.uu.nl

Evolutionary Computation
|March 29, 2007
PubMed
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Coevolutionary algorithms often struggle with monotonic progress. This study introduces the Incremental Pareto-Coevolution Archive (IPCA) and LAyered Pareto-Coevolution Archive (LAPCA) to ensure reliable progress in Pareto-coevolutionary systems.

Area of Science:

  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Coevolutionary algorithms face challenges with monotonic progress due to dynamic evaluation.
  • Achieving consistent progress towards a solution concept is a key open question.

Purpose of the Study:

  • To develop a coevolutionary framework that guarantees monotonic progress towards the Pareto-optimal set.
  • To address the unbounded archive size issue in monotonic coevolutionary approaches.

Main Methods:

  • Introduced the Incremental Pareto-Coevolution Archive (IPCA) based on Evolutionary Multi-Objective Optimization (EMOO).
  • Developed the LAyered Pareto-Coevolution Archive (LAPCA) for bounded archive size and controlled reliability.
  • Evaluated algorithms on a challenging test problem requiring exploration and reliability.

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Main Results:

  • IPCA guarantees monotonicity, avoiding regress even with explorative generators.
  • LAPCA offers a trade-off between archive size and reliability, proving more efficient than IPCA.
  • Demonstrated that approximating monotonic algorithms can yield practical reliability and efficiency.

Conclusions:

  • Monotonic progress in coevolution can be achieved through Pareto-coevolutionary archives.
  • LAPCA provides an efficient and reliable alternative to IPCA for practical applications.
  • This work advances coevolutionary algorithms by enhancing reliability and efficiency.