On the performance metrics of multiobjective optimization
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Expert Systems with Applications: An International Journal
Recombination of similar parents in SMS-EMOA on many-objective 0/1 knapsack problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
An empirical comparison of two common multiobjective reinforcement learning algorithms
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Parameterized average-case complexity of the hypervolume indicator
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A comparison of different algorithms for the calculation of dominated hypervolumes
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Speeding up many-objective optimization by Monte Carlo approximations
Artificial Intelligence
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We describe a new algorithm WFG for calculating hypervolume exactly. WFG is based on the recently-described observation that the exclusive hypervolume of a point $p$ relative to a set $S$ is equal to the difference between the inclusive hypervolume of $p$ and the hypervolume of $S$ with each point limited by the objective values in $p$. WFG applies this technique iteratively over a set to calculate its hypervolume. Experiments show that WFG is substantially faster (in five or more objectives) than all previously-described algorithms that calculate hypervolume exactly.