Publication detail

Evolutionary Development of Generic Sorting Networks by Means of Rewriting Systems

BIDLO, M. DOBEŠ, M.

Original Title

Evolutionary Development of Generic Sorting Networks by Means of Rewriting Systems

Type

journal article in Web of Science

Language

English

Original Abstract

This paper presents an evolutionary developmental method for the design of arbitrarily growing sorting networks. The developmental model is based on a parallel rewriting system (a grammar) that is specified by an alphabet, an initial string (an axiom), and a set of rewriting rules. The rewriting process iteratively expands the axiom in order to develop more complex strings during a series of development steps (i.e., derivations in the grammar). A mapping function is introduced that allows for converting the strings onto comparator structures-building blocks of sorting networks. The construction of the networks is performed in such a way that a given (initial) sorting network grows progressively by adding further building blocks within each development step. For a given (fixed) alphabet, the axiom together with the rewriting rules themselves are the subjects of the evolutionary search. It will be shown that suitable grammars can be evolved for the construction of arbitrarily large sorting networks that grow with various given sizes of development steps. Moreover, the resulting networks exhibit significantly better properties (the number of comparators and delay) in comparison with those obtained by means of similar existing methods.

Keywords

genetic algorithm, development, rewriting system, sorting network, scalability

Authors

BIDLO, M.; DOBEŠ, M.

Released

1. 4. 2020

ISBN

1089-778X

Periodical

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

Year of study

24

Number

2

State

United States of America

Pages from

232

Pages to

244

Pages count

13

URL

BibTex

@article{BUT161834,
  author="Michal {Bidlo} and Michal {Dobeš}",
  title="Evolutionary Development of Generic Sorting Networks by Means of Rewriting Systems",
  journal="IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION",
  year="2020",
  volume="24",
  number="2",
  pages="232--244",
  doi="10.1109/TEVC.2019.2918212",
  issn="1089-778X",
  url="https://ieeexplore.ieee.org/document/8720059"
}