A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms
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| Title: | A Large-Scale Experimental Evaluation of High-Performing Multi- and Many-Objective Evolutionary Algorithms |
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| Authors: | Bezerra, Leonardo, López-Ibáñez, Manuel, Stützle, Thomas |
| Source: | Bezerra, L C T, Lopez-Ibanez, M & Stützle, T 2017, 'A Large-Scale Experimental Evaluation of High-Performing Multi-and Many-Objective Evolutionary Algorithms', Evolutionary Computation. https://doi.org/10.1162/evco_a_00217 |
| Publisher Information: | MIT Press - Journals, 2018. |
| Publication Year: | 2018 |
| Subject Terms: | Multi-objective optimization, Performance assessment, Automatic algorithm configuration, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Evolutionary algorithms, Sciences exactes et naturelles |
| Description: | Research on multi-objective evolutionary algorithms (MOEAs) has produced over the past decades a large number of algorithms and a rich literature on performance assessment tools to evaluate and compare them. Yet, newly proposed MOEAs are typically compared against very few, often a decade older MOEAs. One reason for this apparent contradiction is the lack of a common baseline for comparison, with each subsequent study often devising its own experimental scenario, slightly different from other studies. As a result, the state of the art in MOEAs is a disputed topic. This article reports a systematic, comprehensive evaluation of a large number of MOEAs that covers a wide range of experimental scenarios. A novelty of this study is the separation between the higher-level algorithmic components related to multi-objective optimization (MO), which characterize each particular MOEA, and the underlying parameters—such as evolutionary operators, population size, etc.—whose configuration may be tuned for each scenario. Instead of relying on a common or “default” parameter configuration that may be low-performing for particular MOEAs or scenarios and unintentionally biased, we tune the parameters of each MOEA for each scenario using automatic algorithm configuration methods. Our results confirm some of the assumed knowledge in the field, while at the same time they provide new insights on the relative performance of MOEAs for many-objective problems. For example, under certain conditions, indicator-based MOEAs are more competitive for such problems than previously assumed. We also analyze problem-specific features affecting performance, the agreement between performance metrics, and the improvement of tuned configurations over the default configurations used in the literature. Finally, the data produced is made publicly available to motivate further analysis and a baseline for future comparisons. |
| Document Type: | Article |
| File Description: | application/pdf; No full-text files |
| Language: | English |
| ISSN: | 1530-9304 1063-6560 |
| DOI: | 10.1162/evco_a_00217 |
| Access URL: | https://www.research.manchester.ac.uk/portal/files/61730519/BezLopStu2017assessment.pdf https://pubmed.ncbi.nlm.nih.gov/29155605 https://research.manchester.ac.uk/en/publications/dd900608-9380-48fe-896a-7ec8180c4efa https://doi.org/10.1162/evco_a_00217 https://pubmed.ncbi.nlm.nih.gov/29155605/ https://difusion.ulb.ac.be/vufind/Record/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/283598/Details https://www.mitpressjournals.org/doi/full/10.1162/evco_a_00217 https://core.ac.uk/display/132156078 https://www.ncbi.nlm.nih.gov/pubmed/29155605 https://dblp.uni-trier.de/db/journals/ec/ec26.html#BezerraLS18 https://research.manchester.ac.uk/en/publications/dd900608-9380-48fe-896a-7ec8180c4efa https://doi.org/10.1162/evco_a_00217 https://pure.manchester.ac.uk/ws/files/61730519/BezLopStu2017assessment.pdf |
| Accession Number: | edsair.doi.dedup.....59dffbe88c6dfc54cdad1cfdea35f62c |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Research on multi-objective evolutionary algorithms (MOEAs) has produced over the past decades a large number of algorithms and a rich literature on performance assessment tools to evaluate and compare them. Yet, newly proposed MOEAs are typically compared against very few, often a decade older MOEAs. One reason for this apparent contradiction is the lack of a common baseline for comparison, with each subsequent study often devising its own experimental scenario, slightly different from other studies. As a result, the state of the art in MOEAs is a disputed topic. This article reports a systematic, comprehensive evaluation of a large number of MOEAs that covers a wide range of experimental scenarios. A novelty of this study is the separation between the higher-level algorithmic components related to multi-objective optimization (MO), which characterize each particular MOEA, and the underlying parameters—such as evolutionary operators, population size, etc.—whose configuration may be tuned for each scenario. Instead of relying on a common or “default” parameter configuration that may be low-performing for particular MOEAs or scenarios and unintentionally biased, we tune the parameters of each MOEA for each scenario using automatic algorithm configuration methods. Our results confirm some of the assumed knowledge in the field, while at the same time they provide new insights on the relative performance of MOEAs for many-objective problems. For example, under certain conditions, indicator-based MOEAs are more competitive for such problems than previously assumed. We also analyze problem-specific features affecting performance, the agreement between performance metrics, and the improvement of tuned configurations over the default configurations used in the literature. Finally, the data produced is made publicly available to motivate further analysis and a baseline for future comparisons. |
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| ISSN: | 15309304 10636560 |
| DOI: | 10.1162/evco_a_00217 |