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ABSTRACT

Evolutionary Algorithms (EAs) are powerful metaheuristics that can be applied to almost any optimization problem. However, different Evolutionary Algorithms own different search capabilities that make them more suitable for one or another optimization problem. Furthermore, the combination of several EAs can boost the performance of individual approaches. In this paper we try to exploit the benefits of the combination of several evolutionary approaches by means of a Hybrid Evolutionary Algorithm, where the participation of each individual algorithm on the overall process is dynamically adjusted through its execution. Two different strategies to perform this adjustment are proposed: one with a constant global population size and another one with variable global population size. Experimental results demonstrate that Hybrid Evolutionary Algorithms outperform the individual ones and that the dynamic strategy with variable population size obtains better results on most of the proposed functions.

REFERENCES

• 1: C. Grosan and A. Abraham. Hybrid evolutionary algorithms: Methodologies, architectures, and reviews. In Hybrid Evolutionary Algorithms, volume 75 of Studies in Computational Intelligence, pages 1--17. Springer-Verlag GmbH, 2007.
• 2: A. LaTorre, J.M. Peña, V. Robles, and P. de Miguel. Supercomputer scheduling with innovative evolutionary techniques. In Meta-heuristics for Scheduling: Distributed Computing Environments,volume 146 of Studies in Computational Intelligence. Springer Verlag, Germany, 2008.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.m Miscellaneous

General Terms:
Algorithms, Experimentation

Keywords:
continuous optimization, differential evolution, evolution strategies, genetic algorithms, hybrid evolutionary methods, large scale problems, multiple offspring sampling