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https://hdl.handle.net/2440/72059
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dc.contributor.author | Friedrich, T. | - |
dc.contributor.author | Horoba, C. | - |
dc.contributor.author | Neumann, F. | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | Theoretical Computer Science, 2011; 412(17):1546-1556 | - |
dc.identifier.issn | 0304-3975 | - |
dc.identifier.issn | 1879-2294 | - |
dc.identifier.uri | http://hdl.handle.net/2440/72059 | - |
dc.description.abstract | It is widely assumed that evolutionary algorithms for multi-objective optimization problems should use certain mechanisms to achieve a good spread over the Pareto front. In this paper, we examine such mechanisms from a theoretical point of view and analyze simple algorithms incorporating the concept of fairness. This mechanism tries to balance the number of offspring of all individuals in the current population. We rigorously analyze the runtime behavior of different fairness mechanisms and present illustrative examples to point out situations, where the right mechanism can speed up the optimization process significantly. We also indicate drawbacks for the use of fairness by presenting instances, where the optimization process is slowed down drastically. © 2010 Elsevier B.V. All rights reserved. | - |
dc.description.statementofresponsibility | Tobias Friedrich, Christian Horoba and Frank Neumann | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Science BV | - |
dc.rights | © 2010 Elsevier B.V. All rights reserved. | - |
dc.source.uri | http://dx.doi.org/10.1016/j.tcs.2010.09.023 | - |
dc.title | Illustration of fairness in evolutionary multi-objective optimization | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.tcs.2010.09.023 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Neumann, F. [0000-0002-2721-3618] | - |
Appears in Collections: | Aurora harvest Computer Science publications |
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