Variable size population for dynamic optimization with genetic programming

ABSTRACT

A new model of Genetic Programming with variable size population is presented in this paper and applied to the reconstruction of target functions in dynamic environments (i.e. problems where target functions change with time). The suitability of this model is tested on a set of benchmarks based on some well known symbolic regression problems. Experimental results confirm that our variable size population model finds solutions of the same quality as the ones found by standard Genetic Programming, but with a smaller amount of computational effort.

REFERENCES

• 1: J. Branke. Evolutionary approaches to dynamic optimization problems -- introduction and recent trends. In J. Branke, editor, GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pages 2--4, 2003.
• 2: C. Fernandes, V. Ramos, and A. Rosa. Varying the population size of artificial foraging swarms on time varying landscapes. In International Conference on Artificial Neural Networks: Biological Inspirations, volume 3696 of LNCS, pages 311--316. Springer, 2005.
• 3: M. Keijzer. Improving symbolic regression with interval arithmetic and linear scaling. In C. Ryan et al., editor, Genetic Programming, Proceedings of the 6th European Conference, EuroGP 2003, volume 2610 of LNCS, pages 71--83, Essex, 2003. Springer, Berlin, Heidelberg, New York.
• 4: M. Tomassini, L. Vanneschi, J. Cuendet, and F. Fernández. A new technique for dynamic size populations in genetic programming. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation (CEC'04), pages 486--493, Portland, Oregon, USA, 2004. IEEE Press, Piscataway, NJ.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.2 Automatic Programming
          Subjects: Program synthesis

General Terms:
Algorithms, Performance

Keywords:
dynamic optimization, genetic programming, variable size populations