Many multiple objective genetic algorithms have been developed to approximate the efficient frontier of solutions for multiple objective optimization problems. However, only a limited number of comparison studies have been performed on practical problems. One of the reasons for this may be the lack of commonly accepted measures to compare the solution quality of sets of approximately optimal solutions. In this paper, we perform an extensive set of experiments to quantitatively compare the solutions of two competing algorithms for a bi-criteria parallel machine-scheduling problem.
CITATION STYLE
Matthew Carlyle, W., Kim, B., Fowler, J. W., & Gel, E. S. (2001). Comparison of multiple objective genetic algorithms for parallel machine scheduling problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1993, pp. 472–485). Springer Verlag. https://doi.org/10.1007/3-540-44719-9_33
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