Elitistic Evolution: An Efficient Heuristic for Global Optimization

  • Francisco Viveros Jiménez
  • Efrén Mezura-Montes
  • Alexander Gelbukh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5495)

Abstract

A new evolutionary algorithm, Elitistic Evolution (termed EEv), is proposed in this paper. EEv is an evolutionary method for numerical optimization with adaptive behavior. EEv uses small populations (smaller than 10 individuals). It have an adaptive parameter to adjust the balance between global exploration and local exploitation. Elitism have great influence in EEv’ proccess and that influence is also controlled by the adaptive parameter. EEv’ crossover operator allows a recently generated offspring individual to be parent of other offspring individuals of its generation. It requires the configuration of two user parameters (many state-of-the-art approaches uses at least three). EEv is tested solving a set of 16 benchmark functions and then compared with Differential Evolution and also with some well-known Memetic Algorithms to show its efficiency. Finally, EEv is tested solving a set of 10 benchmark functions with very high dimensionality (50, 100 and 200 dimensions) to show its robustness.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Beyer, H.-G., Schwefel, H.-P.: Evolution strategies: a comprehensive introduction. Natural Computing 1(1) (2002)Google Scholar
  2. 2.
    Storn, R., Price, K.: Differential Evolution - a simple and efficient heuristic for global optimization. Journal of Global Optimization 11(4) (1997)Google Scholar
  3. 3.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. on Evolutionary Computation (1997)Google Scholar
  4. 4.
    Mezura-Montes, E., Coello, C.C.A., Velazquez, R.J.: A comparative study of differential evolution variants for global optimization. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation (2006)Google Scholar
  5. 5.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)MATHGoogle Scholar
  6. 6.
    Viveros, J.F.: DSE: An Hybrid Evolutionary Algorithm with Mathematical Search Method. Special issue journal Research in Computing Science (2008)Google Scholar
  7. 7.
    Noman, N., Iba, H.: Accelerating Differential Evolution Using an Adaptive Local Search. IEEE Transactions on Evol. Comput. 12(1) (2008)Google Scholar
  8. 8.
    Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Nanyang Technol. Univ., Singaporem IIT Kanpur, India, KanGal Rep. 2005005 (2005)Google Scholar
  9. 9.
    Satoh, H., Yamamura, M., Kobayashi, S.: Minimal generation gap models for GAs considering both exploration and exploitation. In: Proc. IIZUKA 1996 (1996)Google Scholar
  10. 10.
    Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10(4) (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Francisco Viveros Jiménez
    • 1
  • Efrén Mezura-Montes
    • 2
  • Alexander Gelbukh
    • 3
  1. 1.Universidad del Istmo Campus IxtepecOaxacaMéxico
  2. 2.Laboratorio Nacional de Informática AvanzadaXalapaMéxico
  3. 3.Centro de Investigación en Computación del Instituto Politécnico NacionalMéxico

Personalised recommendations