Abstract
This study exhaustively compares the abilities to solve manyobjective problems of eight representative algorithms from four different classes (i.e., Pareto-, aggregation-, indicator-, and diversity-based EMO algorithms). The eight compared algorithms are tested on four types of well-defined continuous, discontinuous and combinatorial problems, through three performance metrics as well as a visual observation in the decision space. We can conclude from the experimental results that the performance of the eight algorithms differ not only on the dimensionality of the problems, but also on the shape and features of the Pareto front. From this it suggests an appropriate choice for researchers and practitioners when solving many-objective problems.
Original language | English |
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Title of host publication | GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference |
Editors | Sara Silva |
Publisher | Association for Computing Machinery |
Pages | 1411-1412 |
Number of pages | 2 |
ISBN (Electronic) | 9781450334884 |
DOIs | |
Publication status | Published - 11 Jul 2015 |
Event | 17th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain Duration: 11 Jul 2015 → 15 Jul 2015 |
Publication series
Name | GECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference |
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Conference
Conference | 17th Genetic and Evolutionary Computation Conference, GECCO 2015 |
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Country/Territory | Spain |
City | Madrid |
Period | 11/07/15 → 15/07/15 |
Bibliographical note
Funding Information:The authors wish to thank the support of the National Natural Science Foundation of China (Grant Nos. 61379062, 61372049, 61403326), the Science Research Project of the Education Office of Hunan Province (Grant Nos. 12A135, 12C0378), the Hunan Province Natural Science Foundation (Grant Nos. 14JJ2072, 13JJ8006), the Science and Technology Project of Hunan Province (Grant No. 2014GK3027), the Construct Program of the Key Discipline in Hunan Province, and the Hunan Provincial Innovation Foundation For Post- graduate (Grant No. CX2013A011).
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Artificial Intelligence