Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/109117
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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 1611-3349 |
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 Responsibility: | 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: | http://dx.doi.org/10.1007/978-3-319-13563-2_36 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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