Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Evolutionary multiobjective optimization
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Using unconstrained elite archives for multiobjective optimization
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
Using an adaptation of a binary search tree to improve the NSGA-II nondominated sorting procedure
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
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Given the prominence of elite archiving in contemporary multiobjective optimisation research and the limitations inherent in bounded population sizes, it is unusual that the vast majority of popular techniques aggressively truncate the capacity of archives and are based upon inefficient list representations. By forming better data structures and algorithms for the storage of archival members, the need for truncation is reduced and unbounded elite sets become viable. While work does exist in this vein, it is always of a general nature and significant improvements can be made in the bi-objective case. As such, this paper elucidates the unique properties of two-dimensional non-dominated sets and capitalises on these notions to develop the highly efficient and specialised bi-objective Mak_Tree algorithm. Theoretical results indicate that the specialised approach is preferable to pre-existing general techniques, while empirical analysis illustrates improved performance over both unbounded and bounded list techniques.