Publication detail

Discretization of Decision Variables in Optimization Algorithms

MAREK, M. KADLEC, P.

Original Title

Discretization of Decision Variables in Optimization Algorithms

English Title

Discretization of Decision Variables in Optimization Algorithms

Type

conference paper

Language

en

Original Abstract

This paper presents a verification of universal method for discretization of decision space in optimization algorithms. Real-world optimization tasks frequently use discontinuous decision variables and in order to effectively optimize such tasks, it is necessary to exploit an optimization algorithm that meets such requirement. Unfortunately, very few evolutionary algorithms can naturally work with discontinuous decision space. The method that entitles all optimization algorithms to effectively solve problems with discrete variables is here described and experimentally verified.

English abstract

This paper presents a verification of universal method for discretization of decision space in optimization algorithms. Real-world optimization tasks frequently use discontinuous decision variables and in order to effectively optimize such tasks, it is necessary to exploit an optimization algorithm that meets such requirement. Unfortunately, very few evolutionary algorithms can naturally work with discontinuous decision space. The method that entitles all optimization algorithms to effectively solve problems with discrete variables is here described and experimentally verified.

Keywords

Optimization, evolutionary algorithms, discrete decision space

Released

26.04.2018

Location

Brno

ISBN

978-80-214-5614-3

Book

Proceedings of the 24th Conference STUDENT EEICT 2018

Edition number

1.

Pages from

320

Pages to

324

Pages count

5

BibTex


@inproceedings{BUT147344,
  author="Martin {Marek} and Petr {Kadlec}",
  title="Discretization of Decision Variables in Optimization Algorithms",
  annote="This paper presents a verification of universal method for discretization of decision space
in optimization algorithms. Real-world optimization tasks frequently use discontinuous decision variables
and in order to effectively optimize such tasks, it is necessary to exploit an optimization algorithm
that meets such requirement. Unfortunately, very few evolutionary algorithms can naturally
work with discontinuous decision space. The method that entitles all optimization algorithms to effectively
solve problems with discrete variables is here described and experimentally verified.",
  booktitle="Proceedings of the 24th Conference STUDENT EEICT 2018",
  chapter="147344",
  howpublished="online",
  year="2018",
  month="april",
  pages="320--324",
  type="conference paper"
}