Abstract
We introduce generalized offline orthant search, an algorithmic framework that can be used to solve many problems coming from evolutionary multiobjective optimization using a common well-optimized algorithmic core and relatively cheap reduction procedures. The complexity of the core procedure is O(n · (log n)k-1) for n points of dimension k, and it has a good performance in practice. We show that the presented approach can perform various flavors of non-dominated sorting, dominance counting, evaluate the e-indicator and perform initial fitness assignment for IBEA. It is either competitive with the state-of-the-art algorithms or completely outperforms them for higher problem sizes. We hope that this approach will simplify future development of efficient algorithms for evolutionary multiobjective optimization.
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