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Related Experiment Videos

Where genetic algorithms excel.

E B Baum1, D Boneh, C Garrett

  • 1NEC Research Institute, 4 Independence Way, Princeton, NJ 08540, USA. eric@research.nj.nec.com

Evolutionary Computation
|April 6, 2001
PubMed
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Genetic algorithms (GAs) show superior performance on the Additive Search Problem (ASP), outperforming competitors on a noisy version. Explicitly Parallel Search succeeds where GAs fail to achieve implicit parallelism.

Area of Science:

  • Computational intelligence
  • Machine learning theory
  • Optimization algorithms

Background:

  • Genetic algorithms (GAs) are widely used optimization techniques.
  • The Additive Search Problem (ASP) and its noisy variant present challenges for existing algorithms.
  • Learning the Ising perceptron is a relevant problem reducible to noisy ASP.

Purpose of the Study:

  • To analyze the performance of a genetic algorithm variant, Culling, and other algorithms on the Additive Search Problem (ASP).
  • To investigate the applicability of GAs to a generalized k-ASP and explore the concept of implicit parallelism.
  • To identify conditions under which GAs outperform competing methods.

Main Methods:

  • Performance analysis of a genetic algorithm (Culling) and other algorithms on ASP.

Related Experiment Videos

  • Reduction of Ising perceptron learning to a noisy ASP.
  • Generalization of ASP to k-ASP to study implicit parallelism.
  • Development of Explicitly Parallel Search algorithm.
  • Computation of the optimal culling point for selective breeding.
  • Analysis of a mean field theoretic algorithm.

    • Main Results:

    • A genetic-type algorithm (Culling) outperforms all known competitors on the noisy ASP.
    • GAs do not achieve implicit parallelism on the generalized k-ASP.
    • Explicitly Parallel Search algorithm successfully achieves implicit parallelism.
    • The optimal culling point for selective breeding is independent of fitness function and population distribution.
    • A mean field theoretic algorithm demonstrates performance comparable to Culling on many problems.

    Conclusions:

    • Noisy ASP is a problem where genetic-type algorithms excel.
    • Explicitly Parallel Search offers a successful approach to achieving implicit parallelism.
    • The study provides insights into the strengths and weaknesses of GAs in optimization.
    • Optimal culling points can be determined independently of specific problem parameters.