An efficient approach to unbounded bi-objective archives -: introducing the mak_tree algorithm
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multi-class ROC analysis from a multi-objective optimisation perspective
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Choosing Leaders for Multi-objective PSO Algorithms Using Differential Evolution
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Computers & Mathematics with Applications
AG-ART: An adaptive approach to evolving ART architectures
Neurocomputing
Parallel multi-objective evolutionary algorithms on graphics processing units
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A dominance tree and its application in evolutionary multi-objective optimization
Information Sciences: an International Journal
A fast multi-objective evolutionary algorithm based on a tree structure
Applied Soft Computing
Multi-objective optimization with controlled model assisted evolution strategies
Evolutionary Computation
An improved multiobjective evolutionary algorithm based on dominating tree
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Symbolic archive representation for a fast nondominance test
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
An adaptive multiobjective approach to evolving ART architectures
IEEE Transactions on Neural Networks
Adaptive, convergent, and diversified archiving strategy for multiobjective evolutionary algorithms
Expert Systems with Applications: An International Journal
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
On convergence of the multi-objective particle swarm optimizers
Information Sciences: an International Journal
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
SDMOGA: a new multi-objective genetic algorithm based on objective space divided
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Maxima-finding algorithms for multidimensional samples: A two-phase approach
Computational Geometry: Theory and Applications
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A hybrid adaptive multi-objective memetic algorithm for 0/1 knapsack problem
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A hybrid self-adjusted memetic algorithm for multi-objective optimization
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
A hybrid multiobjective evolutionary algorithm: Striking a balance with local search
Mathematical and Computer Modelling: An International Journal
BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems
Computers and Operations Research
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Multiobjective evolutionary algorithms (MOEAs) have been the subject of numerous studies over the past 20 years. Recent work has highlighted the use of an active archive of elite, nondominated solutions to improve the optimization speed of these algorithms. However, preserving all elite individuals is costly in time (due to the linear comparison with all archived solutions needed before a new solution can be inserted into the archive). Maintaining an elite population of a fixed maximum size (by clustering or other means) alleviates this problem, but can cause retreating (or oscillitory) and shrinking estimated Pareto fronts - which can affect the efficiency of the search process. New data structures are introduced to facilitate the use of an unconstrained elite archive, without the need for a linear comparison to the elite set for every new individual inserted. The general applicability of these data structures is shown by their use in an evolution-strategy-based MOEA and a genetic-algorithm-based MOEA. It is demonstrated that MOEAs using the new data structures run significantly faster than standard, unconstrained archive MOEAs, and result in estimated Pareto fronts significantly ahead of MOEAs using a constrained archive. It is also shown that the use of an unconstrained elite archive permits robust criteria for algorithm termination to be used, and that the use of the data structure can also be used to increase the speed of algorithms using ε-dominance methods.