报告内容
框架
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Multi-objective evolutionary algorithms have been verified
to be a useful technology for solving optimization problems during the last
two decades, however, much work still deserves further investigations when
addressing complex optimization tasks. In this talk, I will first briefly
introduce the multi-objective evolutionary algorithms, and then mainly focus
on three multi-objective evolutionary algorithms recently suggested by us to
tackle complex optimization problems. The three works included in this
presentation are: 1) a knee point driven evolutionary algorithm for
many-objective optimization problems, 2) a decision variable clustering based
evolutionary algorithm for large-scale optimization problems, and 3) a
multi-objective evolutionary algorithm for task-oriented pattern mining task.
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