A comparison between representations for evolving images

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Evolving images using genetic programming is a complex task and the representation of the solutions has an important impact on the performance of the system. In this paper, we present two novel representations for evolving images with genetic programming. Both these representations are based on the idea of recursively partitioning the space of an image. This idea distinguishes these representations from the ones that are currently most used in the literature. The first representation that we introduce partitions the space using rectangles, while the second one partitions using triangles. These two representations are compared to one of the most well known and frequently used expression-based representations, on five different test cases. The presented results clearly indicate the appropriateness of the proposed representations for evolving images. Also, we give experimental evidence of the fact that the proposed representations have a higher locality compared to the compared expression-based representation.

Original languageEnglish
Title of host publicationEvolutionary and Biologically Inspired Music, Sound, Art and Design - 5th International Conference, EvoMUSART 2016, Proceedings
PublisherSpringer-Verlag
Pages163-185
Number of pages23
Volume9596
ISBN (Print)9783319310077
DOIs
Publication statusPublished - 2016
Event5th International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2016 - Porto, Portugal
Duration: 30 Mar 20161 Apr 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9596
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference5th International Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, EvoMUSART 2016
CountryPortugal
CityPorto
Period30/03/161/04/16

Keywords

  • Genetic programming (GP)
  • Image representation
  • Locality

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