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Scalability problems of simple genetic algorithms.

D Thierens1

  • 1Department of Computer Science, Utrecht University, P.O. Box 80089, 3508 TB Utrecht, The Netherlands. dirk.thierens@cs.uu.nl

Evolutionary Computation
|December 1, 1999
PubMed
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Scalable genetic algorithms (GAs) face challenges due to building block mixing limitations. Problem difficulty increases with size, restricting reliable convergence unless building blocks are tightly linked.

Area of Science:

  • Evolutionary Computation
  • Artificial Intelligence
  • Computer Science

Background:

  • Scalability is a critical challenge in algorithmic design, particularly for genetic algorithms (GAs).
  • Understanding the limitations of simple genetic algorithms (SGAs) is crucial for developing more competent GAs.

Purpose of the Study:

  • To provide insight into the scalability problems of simple genetic algorithms.
  • To analyze the impact of building block mixing on GA performance.

Main Methods:

  • Analysis of building block mixing in genetic algorithms.
  • Examination of the interplay between mixing boundaries and schema theorem boundaries.
  • Evaluation of extensions like elitism, niching, and restricted mating.

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Main Results:

  • The need for building block mixing imposes a boundary in the GA parameter space.
  • This boundary, combined with the schema theorem, defines a region for reliable convergence.
  • This convergence region shrinks rapidly with increasing problem size unless building blocks are tightly linked.
  • Standard extensions (elitism, niching, restricted mating) do not significantly improve scalability.

Conclusions:

  • Simple genetic algorithms exhibit inherent scalability limitations, particularly concerning building block mixing.
  • Problem coding structure, specifically the linkage of building blocks, is critical for GA scalability.
  • Straightforward extensions of SGAs do not resolve fundamental scalability issues.