Evolutionary algorithms with clustering for dynamic fitness landscapes

Authors

  • Victoria S. Aragón Lab. de Investigación y Desarrollo en Inteligencia Computacional (LIDIC), Universidad Nacional de San Luis, San Luis, Argentina
  • Susana Cecilia Esquivel Lab. de Investigación y Desarrollo en Inteligencia Computacional (LIDIC), Universidad Nacional de San Luis, San Luis, Argentina

Keywords:

Dynamic Multimodal Functions, Evolutionary Algorithms, Clustering Algorithms, Macromutation

Abstract

Interest on dynamic multimodal functions risen over the last years since many real problems have this feature. On these problems, the goal is no longer to find the global optimal, but to track their progression through the space as closely as possible. This paper presents three evolutionary algorithms for dynamic fitness landscapes. In order to maintain diversity in the population they use two clustering techniques and a macromutation operator. Besides, this paper compares two crossover operators: arithmetic and multiparents two points, respectively. Effectiveness and limitations of each algorithm are discuss anda analyzed.

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Published

2005-12-01

How to Cite

Aragón, V. S., & Esquivel, S. C. (2005). Evolutionary algorithms with clustering for dynamic fitness landscapes. Journal of Computer Science and Technology, 5(04), p. 196–203. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/836

Issue

Vol. 5 No. 04 (2005): Sixteenth Issue

Section

Original Articles

License

 

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