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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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 20; H. 3; S. 403 - 417
Hauptverfasser: Bezerra, Leonardo C. T., Lopez-Ibanez, Manuel, Stutzle, Thomas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
multiobjective optimization
Automatic algorithm configuration
permutation flow shop problem (PFSP)
evolutionary algorithms
ISSN:1089-778X, 1941-0026
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2015.2474158