Fitness landscape analysis in the optimization of coefficients of curve parametrizations
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/45090DOI: 10.1007/978-3-319-74718-7_56
ISBN: 978-3-319-74717-0
Publisher
Springer
Date
2018-01-26Funders
Ministerio de Economía y Competitividad
Austrian Research Promotion Agency
Bibliographic citation
Winkler, S.M. & Sendra, J. R. 2018, “Fitness landscape analysis in the optimization of coefficients of curve parametrizations”, in Computer Aided Systems Theory-EUROCAST 2017, EUROCAST 2017. Lecture Notes in Computer Science, vol. 10671, pp. 464-472
Description / Notes
Este documento se considera que es una ponencia de congresos en lugar de un capítulo de libro.
Computer Aided Systems Theory - EUROCAST 2017, 19-24 February, Las Palmas de Gran Canaria, Spain.
J.R. Sendra is member of the Research Group ASYNACS (Ref.CT-CE2019/683)
Project
info:eu-repo/grantAgreement/MINECO//MTM2014-54141-P/ES/CONSTRUCCIONES ALGEBRO-GEOMETRICAS: FUNDAMENTOS, ALGORITMOS Y APLICACIONES/
HOPL (Austrian Research Promotion Agency FFG #843532).
Document type
info:eu-repo/semantics/conferenceObject
Version
info:eu-repo/semantics/acceptedVersion
Publisher's version
https://doi.org/10.1007/978-3-319-74718-7_56Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© Springer International Publishing AG 2018
Access rights
info:eu-repo/semantics/openAccess
Abstract
Parametric representations of geometric objects, such as curves or surfaces, may have unnecessarily huge integer coefficients. Our goal is to search for an alternative parametric representation of the same object with significantly smaller integer coefficients. We have developed and implemented an evolutionary algorithm that is able to find solutions to this problem in an efficient as well as robust way. In this paper we analyze the fitness landscapes associated with this evolutionary algorithm. We here discuss the use of three different strategies that are used to evaluate and order partial solutions. These orderings lead to different landscapes of combinations of partial solutions in which the optimal solutions are searched. We see that the choice of this ordering strategy has a huge inuence on the characteristics of the resulting landscapes, which are in this paper analyzed using a set of metrics, and also on the quality of the solutions that can be found by the subsequent evolutionary search.
Files in this item
Files | Size | Format |
|
---|---|---|---|
Fitness_Winkler_EUROCAST2017_2 ... | 2.321Mb |
|
Files | Size | Format |
|
---|---|---|---|
Fitness_Winkler_EUROCAST2017_2 ... | 2.321Mb |
|