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A new approach to population sizing for memetic algorithms: a case study for the multidimensional assignment problem.

Daniel Karapetyan1, Gregory Gutin

  • 1Department of Computer Science, Royal Holloway London University, Egham, Surrey, United Kingdom. Daniel.Karapetyan@gmail.com

Evolutionary Computation
|September 28, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an adjustable population size for memetic algorithms, enhancing their performance on complex optimization problems. This dynamic approach optimizes algorithm quality across various instances and runtimes without extra tuning.

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Area of Science:

  • Artificial Intelligence
  • Operations Research
  • Computer Science

Background:

  • Memetic algorithms are effective for hard optimization problems.
  • Fixed population sizes limit algorithm performance across diverse problem instances and runtimes.
  • Optimal population size is instance- and procedure-dependent.

Purpose of the Study:

  • To propose an adjustable population size mechanism for memetic algorithms.
  • To enhance memetic algorithm flexibility and efficiency for varying runtimes and local search procedures.
  • To develop a self-tuning approach for memetic algorithm population sizing.

Main Methods:

  • An adjustable population size is calculated based on total algorithm runtime and average local search runtime.
  • Runtime bounds are incorporated as algorithm parameters.
  • Coefficients are tuned offline, independent of specific instances and local search methods.
  • The approach is applied to develop a memetic algorithm for the multidimensional assignment problem (MAP).

Main Results:

  • The adjustable population size allows memetic algorithms to perform efficiently across a wide range of running times.
  • The proposed method demonstrates flexibility with different local search procedures.
  • No additional instance-specific tuning is required for the developed algorithm.
  • Effective performance was shown for the multidimensional assignment problem.

Conclusions:

  • Adjustable population size significantly improves memetic algorithm adaptability and performance.
  • The proposed method offers a robust and flexible solution for memetic algorithm design.
  • This approach reduces the need for manual parameter tuning, enhancing usability.