Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: A feature-based analysis on the impact of linear constraints for ε-constrained differential evolution
Author: Poursoltan, S.
Neumann, F.
Citation: IEEE Transactions on Evolutionary Computation, 2014, pp.3088-3095
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2014
ISBN: 9781479914883
ISSN: 1089-778X
Conference Name: 2014 IEEE Congress on Evolutionary Computation (CEC 2014) (6 Jul 2014 - 11 Jul 2014 : Beijing, China)
Statement of
Shayan Poursoltan, Frank Neumann
Abstract: Feature-based analysis has provided new insights into what characteristics make a problem hard or easy for a given algorithms. Studies, so far, considered unconstrained continuous optimisation problem and classical combinatorial optimisation problems such as the Travelling Salesperson problem. In this paper, we present a first feature-based analysis for constrained continuous optimisation. To start the feature-based analysis of constrained continuous optimization, we examine how linear constraints can influence the optimisation behaviour of the wellknown e-constrained differential evolution algorithm. Evolving the coefficients of a linear constraint, we show that even the type of one linear constraint can make a difference of 10-30% in terms of function evaluations for well-known continuous benchmark functions.
Keywords: Constraints, continuous optimisation, difficulty prediction, linear constraints, features
Rights: © 2014 IEEE
DOI: 10.1109/CEC.2014.6900572
Grant ID:
Published version:
Appears in Collections:Aurora harvest 3
Computer Science publications

Files in This Item:
File Description SizeFormat 
  Restricted Access
Restricted Access457.56 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.