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Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes
2017
Evolutionary Computation
In this paper, we attempt to understand and to contrast the impact of problem features on the performance of randomized search heuristics for black-box multi-objective combinatorial optimization problems. At first, we measure the performance of two conventional dominance-based approaches with unbounded archive on a benchmark of enumerable binary optimization problems with tunable ruggedness, objective space dimension, and objective correlation (ρMNK-landscapes). Precisely, we investigate the
doi:10.1162/evco_a_00193
pmid:27689467
fatcat:7lwr4sdq3fajtnni53vdkibd6a