A selection method for evolutionary algorithms based on the Golden Section
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- @Article{CUEVAS:2018:ESA,
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author = "Erik Cuevas and Luis Enriquez and Daniel Zaldivar and
Marco Perez-Cisneros",
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title = "A selection method for evolutionary algorithms based
on the Golden Section",
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journal = "Expert Systems with Applications",
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volume = "106",
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pages = "183--196",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Evolutionary
algorithms, Golden Section, Selection methods,
Evolutionary strategies (ES), Evolutionary
computation",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2018.03.064",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957417418302215",
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abstract = "During millions of years, nature has developed
patterns and processes with interesting
characteristics. They have been used as inspiration for
a significant number of innovative models that can be
extended to solve complex engineering and mathematical
problems. One of the most famous patterns present in
nature is the Golden Section (GS). It defines an
especial proportion that allows the adequate formation,
selection, partition, and replication in several
natural phenomena. On the other hand, Evolutionary
algorithms (EAs) are stochastic optimization methods
based on the model of natural evolution. One important
process in these schemes is the operation of selection
which exerts a strong influence on the performance of
their search strategy. Different selection methods have
been reported in the literature. However, all of them
present an unsatisfactory performance as a consequence
of the deficient relations between elitism and
diversity of their selection procedures. In this paper,
a new selection method for evolutionary computation
algorithms is introduced. In the proposed approach, the
population is segmented into several groups. Each group
involves a certain number of individuals and a
probability to be selected, which are determined
according to the GS proportion. Therefore, the
individuals are divided into categories where each
group contains individual with similar quality
regarding their fitness values. Since the possibility
to choose an element inside the group is the same, the
probability of selecting an individual depends
exclusively on the group from which it belongs. Under
these conditions, the proposed approach defines a
better balance between elitism and diversity of the
selection strategy. Numerical simulations show that the
proposed method achieves the best performance over
other selection algorithms, in terms of its solution
quality and convergence speed",
- }
Genetic Programming entries for
Erik Cuevas
Luis Enriquez
Daniel Zaldivar
Marco Perez-Cisneros
Citations