Detail publikace

Genetic Algorithm using Theory of Chaos

Originální název

Genetic Algorithm using Theory of Chaos

Anglický název

Genetic Algorithm using Theory of Chaos

Jazyk

en

Originální abstrakt

This paper is focused on genetic algorithm with chaotic crossover operator. We have performed some experiments to study possible use of chaos in simulated evolution. A novel genetic algorithm with chaotic optimization operation is proposed to optimization of multimodal functions. As the basis of a new crossing operator a simple equation involving chaos is used, concrete the logistic function. The logistic function is a simple one-parameter function of the second order that shows a chaotic behavior for some values of the parameter. Generally, solution of the logistic function has three areas of its behavior: convergent, periodic and chaotic. We have supposed that the convergent behavior leads to exploitation and the chaotic behavior aids to exploration. The periodic behavior is probably neutral and thus it is a negligible one. Results of our experiments conrm these expectations. A proposed genetic algorithm with chaotic crossover operator leads to a more ecient computation in comparison with the traditional genetic algorithm.

Anglický abstrakt

This paper is focused on genetic algorithm with chaotic crossover operator. We have performed some experiments to study possible use of chaos in simulated evolution. A novel genetic algorithm with chaotic optimization operation is proposed to optimization of multimodal functions. As the basis of a new crossing operator a simple equation involving chaos is used, concrete the logistic function. The logistic function is a simple one-parameter function of the second order that shows a chaotic behavior for some values of the parameter. Generally, solution of the logistic function has three areas of its behavior: convergent, periodic and chaotic. We have supposed that the convergent behavior leads to exploitation and the chaotic behavior aids to exploration. The periodic behavior is probably neutral and thus it is a negligible one. Results of our experiments conrm these expectations. A proposed genetic algorithm with chaotic crossover operator leads to a more ecient computation in comparison with the traditional genetic algorithm.

BibTex


@article{BUT119804,
  author="Petra {Snášelová} and František {Zbořil}",
  title="Genetic Algorithm using Theory of Chaos",
  annote="
This paper is focused on genetic algorithm with chaotic crossover operator. We
have performed some experiments to study possible use of chaos in simulated
evolution. A novel genetic algorithm with chaotic optimization operation is
proposed to optimization of multimodal functions. As the basis of a new crossing
operator a simple equation involving chaos is used, concrete the logistic
function. The logistic function is a simple one-parameter function of the second
order that shows a chaotic behavior for some values of the parameter. Generally,
solution of the logistic function has three areas of its behavior: convergent,
periodic and chaotic. We have supposed that the convergent behavior leads to
exploitation and the chaotic behavior aids to exploration. The periodic behavior
is probably neutral and thus it is a negligible one. Results of our experiments
conrm these expectations. A proposed genetic algorithm with chaotic crossover
operator leads to a more ecient computation in comparison with the traditional
genetic algorithm.",
  address="NEUVEDEN",
  chapter="119804",
  doi="10.1016/j.procs.2015.05.248",
  edition="NEUVEDEN",
  howpublished="print",
  institution="NEUVEDEN",
  number="51",
  volume="2015",
  year="2015",
  month="june",
  pages="316--325",
  publisher="NEUVEDEN",
  type="journal article in Web of Science"
}