R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection
An indicator-based evolutionary multiobjective optimization algorithm (EMOA) is introduced which incorporates the contribution to the unary R2-indicator as the secondary selection criterion. First experiments indicate that the R2-EMOA accurately approximates the Pareto front of the considered contin...
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Published in | Learning and Intelligent Optimization Vol. 7997; pp. 70 - 74 |
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Main Authors | , , |
Format | Book Chapter |
Language | English |
Published |
Germany
Springer Berlin / Heidelberg
2013
Springer Berlin Heidelberg |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Abstract | An indicator-based evolutionary multiobjective optimization algorithm (EMOA) is introduced which incorporates the contribution to the unary R2-indicator as the secondary selection criterion. First experiments indicate that the R2-EMOA accurately approximates the Pareto front of the considered continuous multiobjective optimization problems. Furthermore, decision makers’ preferences can be included by adjusting the weight vector distributions of the indicator which results in a focused search behavior. |
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AbstractList | An indicator-based evolutionary multiobjective optimization algorithm (EMOA) is introduced which incorporates the contribution to the unary R2-indicator as the secondary selection criterion. First experiments indicate that the R2-EMOA accurately approximates the Pareto front of the considered continuous multiobjective optimization problems. Furthermore, decision makers’ preferences can be included by adjusting the weight vector distributions of the indicator which results in a focused search behavior. |
Author | Trautmann, Heike Wagner, Tobias Brockhoff, Dimo |
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SubjectTerms | EMOA Indicator-based selection Multiobjective optimization Performance assessment Preferences R2-indicator |
Title | R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection |
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