Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: On the impact of utility functions in interactive evolutionary multi-objective optimization
Author: Neumann, F.
Nguyen, A.
Citation: Lecture Notes in Artificial Intelligence, 2014 / Dick, G., Browne, W.N., Whigham, P., Zhang, M., Bui, L.T., Ishibuchi, H., Jin, Y., Li, X., Shi, Y., Singh, P., Tan, K.C., Tang, K. (ed./s), vol.8886, pp.419-430
Publisher: Springer Verlag
Issue Date: 2014
Series/Report no.: Lecture Notes in Computer Science (LNCS, vol. 8886)
ISBN: 978-3-319-13562-5
ISSN: 0302-9743
Conference Name: 10th International Conference on Simulated Evolution and Learning (SEAL 2014) (15 Dec 2014 - 18 Dec 2014 : Dunedin, New Zealand)
Editor: Dick, G.
Browne, W.N.
Whigham, P.
Zhang, M.
Bui, L.T.
Ishibuchi, H.
Jin, Y.
Li, X.
Shi, Y.
Singh, P.
Tan, K.C.
Tang, K.
Statement of
Frank Neumann and Anh Quang Nguyen
Abstract: Interactive evolutionary algorithms for multi-objective optimization have gained an increasing interest in recent years. As multiobjective optimization usually deals with the optimization of conflicting objectives, a decision maker is involved in the optimization process when encountering incomparable solutions. We study the impact of a decision maker from a theoretical perspective and analyze the runtime of evolutionary algorithms until they have produced for the first time a Pareto optimal solution with the highest preference of the decision maker. Considering the linear decision maker, we show that many multi-objective optimization problems are not harder than their single-objective counterpart. Interestingly, this does not hold for a decision maker using the Chebeyshev utility function. Furthermore, we point out situations where evolutionary algorithms involving a linear decision maker have difficulties in producing an optimal solution even if the underlying single-objective problems are easy to be solved by simple evolutionary algorithms.
Rights: © Springer International Publishing Switzerland 2014
DOI: 10.1007/978-3-319-13563-2_36
Published version:
Appears in Collections:Aurora harvest 3
Computer Science publications

Files in This Item:
File Description SizeFormat 
  Restricted Access
Restricted Access218.13 kBAdobe PDFView/Open

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