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A Favorable Weight-Based Evolutionary Algorithm for Multiple Criteria Problems
Date
2010-04-01
Author
SOYLU, Banu
Köksalan, Mustafa Murat
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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In this paper, we present a favorable weight-based evolutionary algorithm for multiple criteria problems. The algorithm tries to both approximate the Pareto frontier and evenly distribute the solutions over the frontier. These two goals are common for many multiobjective evolutionary algorithms. To achieve these goals in our algorithm, each member selects its own weights for a weighted Tchebycheff distance function to define its fitness score. The fitness scores favor solutions that are closer to the Pareto frontier and that are located at underrepresented regions. We compare the performance of the algorithm with two leading evolutionary algorithms on various continuous test problems having different number of criteria.
Subject Keywords
Theoretical Computer Science
,
Computational Theory and Mathematics
,
Software
URI
https://hdl.handle.net/11511/57962
Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
DOI
https://doi.org/10.1109/tevc.2009.2027357
Collections
Department of Industrial Engineering, Article