Hybrid Two-Population Genetic Algorithm (original) (raw)

Abstract

Genetic Algorithms are non-deterministic, stochastic-search adaptive methods which use the theories of natural evolution and selection in order to solve a problem within a complex range of possible solutions. The aim is to control the distribution of the search space by incorporating an exhaustive method in order to maintain a constant evolution of the population. The main goal is that of redesigning the algorithm in order to add to the classic genetic algorithm method those characteristics which favour exhaustive search methods. The method explained guarantees the achievement of reasonably satisfactory solutions in short time-spans and in a deterministic way, which entails that successive repetitions of the algorithm will achieve the same solutions in almost constant time-spans. We are, therefore, dealing with an evolutionary technique which makes the most of the characteristics of genetic algorithms and exhaustive methods.

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Author information

Authors and Affiliations

  1. Univ. da Coruña, Facultad Informática, Campus Elviña, 15071, A Coruña, Spain
    Julian Dorado, Antonino Santos, Juan R. Rabuñal, Nieves Pedreira & Alejandro Pazos

Authors

  1. Julian Dorado
  2. Antonino Santos
  3. Juan R. Rabuñal
  4. Nieves Pedreira
  5. Alejandro Pazos

Editor information

Editors and Affiliations

  1. Computer Science I, University of Dortmund, 44221, Dortmund, Germany
    Bernd Reusch

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© 2001 Springer-Verlag Berlin Heidelberg

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Dorado, J., Santos, A., Rabuñal, J.R., Pedreira, N., Pazos, A. (2001). Hybrid Two-Population Genetic Algorithm. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4\_47

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