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Content Provider | ACM Digital Library |
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Author | Wegener, Ingo Neumann, Frank |
Abstract | Many real-world problems are multi-objective optimization problems and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front, multi-objective optimization problems are considered as more difficult than single-objective problems. One should not forget that the fitness vector with respect to more than one objective contains more information that in principle can direct the search of evolutionary algorithms. Therefore, it is possible that a single-objective problem can be solved more efficiently via a generalized multi-objective model of the problem. That this is indeed the case is proved by investigating the computation of minimum spanning trees. |
Starting Page | 763 |
Ending Page | 769 |
Page Count | 7 |
File Format | |
ISBN | 1595930108 |
DOI | 10.1145/1068009.1068139 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2005-06-25 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Running time analysis Working principles of evolutionary computing Multi-objective optimization |
Content Type | Text |
Resource Type | Article |
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