Publication result detail

Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP

DRAHOŠOVÁ, M.; SEKANINA, L.

Original Title

Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP

English Title

Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP

Type

Peer-reviewed article not indexed in WoS or Scopus

Original Abstract

The aim of this paper is to accelerate the task of evolutionary image filter design using coevolution of candidate filters and training vectors subsets. Two coevolutionary methods are implemented and compared for this task in the framework of Cartesian Genetic Programming (CGP). Experimental results show that only 15-20 % of original test vectors are needed to find an image filter which provides the same quality of filtering as the best filter evolved using the standard CGP which utilizes the whole training set. Moreover, the median time of evolution was reduced 2.99 times in comparison with the standard CGP.

English abstract

The aim of this paper is to accelerate the task of evolutionary image filter design using coevolution of candidate filters and training vectors subsets. Two coevolutionary methods are implemented and compared for this task in the framework of Cartesian Genetic Programming (CGP). Experimental results show that only 15-20 % of original test vectors are needed to find an image filter which provides the same quality of filtering as the best filter evolved using the standard CGP which utilizes the whole training set. Moreover, the median time of evolution was reduced 2.99 times in comparison with the standard CGP.

Keywords

Cartesian genetic programming, coevolution, fitness modeling, image filter design.

Key words in English

Cartesian genetic programming, coevolution, fitness modeling, image filter design.

Authors

DRAHOŠOVÁ, M.; SEKANINA, L.

RIV year

2013

Released

24.07.2012

Publisher

Springer Verlag

Location

Berlin

ISBN

978-3-642-32936-4

Book

The 12th International Conference on Parallel Problem Solving from Nature

ISBN

0302-9743

Periodical

Lecture Notes in Computer Science

Volume

2012

Number

7491

State

Federal Republic of Germany

Pages from

163

Pages to

172

Pages count

10

URL

http://link.springer.com/chapter/10.1007%2F978-3-642-32937-1_17

BibTex

@article{BUT96949,
  author="Michaela {Drahošová} and Lukáš {Sekanina}",
  title="Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP",
  journal="Lecture Notes in Computer Science",
  year="2012",
  volume="2012",
  number="7491",
  pages="163--172",
  doi="10.1007/978-3-642-32937-1\{_}17",
  issn="0302-9743",
  url="http://link.springer.com/chapter/10.1007%2F978-3-642-32937-1_17"
}