Optimization in a distributed processing environment using genetic algorithms with multivariate crossover

ABSTRACT

We set out to demonstrate the effectiveness of distributed genetic algorithms using multivariate crossover in optimizing a function of a sizable number of independent variables. Our results show that this algorithm has unique potential in optimizing such functions. The multivariate crossover meta-strategy, however, did not result in a singularly better performance of the algorithm than did simpler crossover strategies.

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INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Probabilistic algorithms (including Monte Carlo)

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization

General Terms:
Experimentation, Measurement, Theory