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ABSTRACT

This work studies the mGA operator (Micro Genetic Algorithm), that has been proposed in literature as a "local search" operator for optimization with Genetic Algorithm. A new interpretation for this operator behavior is proposed, showing the role that this operator can have in a "global search". Such interpretation will possibly allow the definition of some directives for this operator parameter tuning, leading to more efficient GA that reach the optima with greater probability, spending less objective function evaluations. Some preliminary tests, conducted over problems of nonlinear functions with continuous variables, are presented, leading to some specific conjectures about what should be such directives.

REFERENCES

• 1: S. A. Kazarlis, S. E. Papadakis, J. B. Theocharis, and V. Petridis. Microgenetic algorithms as generalized hill-climbing operators for GA optimization. IEEE Trans. Evol. Comput., 5(3):204--217, 2001.
• 2: R. H. C. Takahashi, J. A. Vasconcelos, J. A. Ramirez, and L. Krahenbuhl. A multiobjective methodology for evaluating genetic operators. IEEE Trans. Magn., 37(5):3414--3417, 2003.

INDEX TERMS

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

General Terms:
Algorithms

Keywords:
genetic algorithms, local search, micro genetic algorithm