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Difficulty in Evolutionary Multiobjective Optimization of Discrete Objective Functions with Different Granularities.

, , and . EMO, volume 7811 of Lecture Notes in Computer Science, page 230-245. Springer, (2013)

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Effects of discrete objective functions with different granularities on the search behavior of EMO algorithms., , and . GECCO, page 481-488. ACM, (2012)Rule weight update in parallel distributed fuzzy genetics-based machine learning with data rotation., , and . FUZZ-IEEE, page 1-8. IEEE, (2013)Two-objective solution set optimization to maximize hypervolume and decision space diversity in multiobjective optimization., , , and . SCIS&ISIS, page 1871-1876. IEEE, (2012)Difficulty in Evolutionary Multiobjective Optimization of Discrete Objective Functions with Different Granularities., , and . EMO, volume 7811 of Lecture Notes in Computer Science, page 230-245. Springer, (2013)Comparison of different fitness functions in genetic fuzzy rule selection., , , , and . SCIS&ISIS, page 1046-1051. IEEE, (2012)Many-objective and many-variable test problems for visual examination of multiobjective search., , , and . IEEE Congress on Evolutionary Computation, page 1491-1498. IEEE, (2013)Effects of duplicated objectives in many-objective optimization problems on the search behavior of hypervolume-based evolutionary algorithms., , and . MCDM, page 25-32. IEEE, (2013)Learning from multiple data sets with different missing attributes and privacy policies: Parallel distributed fuzzy genetics-based machine learning approach., , and . IEEE BigData, page 63-70. IEEE Computer Society, (2013)Ensemble Fuzzy Rule-Based Classifier Design by Parallel Distributed Fuzzy GBML Algorithms., , and . SEAL, volume 7673 of Lecture Notes in Computer Science, page 93-103. Springer, (2012)