Automatic Component-Wise Design of Multiobjective Evolutionary Algorithms
Multiobjective evolutionary algorithms (MOEAs) are typically proposed, studied, and applied as monolithic blocks with a few numerical parameters that need to be set. Few works have studied how the algorithmic components of these evolutionary algorithms can be classified and combined to produce new a...
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| Published in: | IEEE transactions on evolutionary computation Vol. 20; no. 3; pp. 403 - 417 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1089-778X, 1941-0026 |
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| Abstract | Multiobjective evolutionary algorithms (MOEAs) are typically proposed, studied, and applied as monolithic blocks with a few numerical parameters that need to be set. Few works have studied how the algorithmic components of these evolutionary algorithms can be classified and combined to produce new algorithmic designs. The motivation for studies of this latter type stem from the development of flexible software frameworks and the usage of automatic algorithm configuration methods to find novel algorithm designs. In this paper, we propose an MOEA template and a new conceptual view of its components that surpasses existing frameworks in both number of algorithms that can be instantiated from the template and flexibility to produce novel algorithmic designs. We empirically demonstrate the flexibility of our proposed framework by automatically designing MOEAs for continuous and combinatorial optimization problems. The automatically designed algorithms are often able to outperform six traditional MOEAs from the literature, even after tuning their numerical parameters. |
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| AbstractList | Multiobjective evolutionary algorithms (MOEAs) are typically proposed, studied, and applied as monolithic blocks with a few numerical parameters that need to be set. Few works have studied how the algorithmic components of these evolutionary algorithms can be classified and combined to produce new algorithmic designs. The motivation for studies of this latter type stem from the development of flexible software frameworks and the usage of automatic algorithm configuration methods to find novel algorithm designs. In this paper, we propose anMOEA template and a new conceptual view of its components that surpasses existing frameworks in both number of algorithms that can be instantiated from the template and flexibility to produce novel algorithmic designs. We empirically demonstrate the flexibility of our proposed framework by automatically designing MOEAs for continuous and combinatorial optimization problems. The automatically designed algorithms are often able to outperform six traditional MOEAs from the literature, even after tuning their numerical parameters. Multiobjective evolutionary algorithms (MOEAs) are typically proposed, studied, and applied as monolithic blocks with a few numerical parameters that need to be set. Few works have studied how the algorithmic components of these evolutionary algorithms can be classified and combined to produce new algorithmic designs. The motivation for studies of this latter type stem from the development of flexible software frameworks and the usage of automatic algorithm configuration methods to find novel algorithm designs. In this paper, we propose an MOEA template and a new conceptual view of its components that surpasses existing frameworks in both number of algorithms that can be instantiated from the template and flexibility to produce novel algorithmic designs. We empirically demonstrate the flexibility of our proposed framework by automatically designing MOEAs for continuous and combinatorial optimization problems. The automatically designed algorithms are often able to outperform six traditional MOEAs from the literature, even after tuning their numerical parameters. |
| Author | Lopez-Ibanez, Manuel Bezerra, Leonardo C. T. Stutzle, Thomas |
| Author_xml | – sequence: 1 givenname: Leonardo C. T. surname: Bezerra fullname: Bezerra, Leonardo C. T. email: lteonaci@ulb.ac.be organization: IRIDIA Lab., Univ. Libre de Bruxelles, Brussels, Belgium – sequence: 2 givenname: Manuel surname: Lopez-Ibanez fullname: Lopez-Ibanez, Manuel email: manuel.lopez-ibanez@ulb.ac.be organization: IRIDIA Lab., Univ. Libre de Bruxelles, Brussels, Belgium – sequence: 3 givenname: Thomas surname: Stutzle fullname: Stutzle, Thomas email: stuetzle@ulb.ac.be organization: IRIDIA Lab., Univ. Libre de Bruxelles, Brussels, Belgium |
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| Keywords | multiobjective optimization Automatic algorithm configuration permutation flow shop problem (PFSP) evolutionary algorithms |
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| SubjectTerms | Algorithm design and analysis Algorithms automatic algorithm configuration Automation Combinatorial analysis Evolutionary algorithms Evolutionary computation Flexibility Genetic algorithms Mathematical models Measurement Multiobjective optimization Optimization permutation flowshop problem Proposals Sociology Software algorithms Statistics Tuning |
| Title | Automatic Component-Wise Design of Multiobjective Evolutionary Algorithms |
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