On the Average Number of Maxima in a Set of Vectors and Applications
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Random Structures & Algorithms
An adaptive divide-and-conquer methodology for evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Compressed-objective genetic algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Distance Based Ranking in Many-Objective Particle Swarm Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Parallel Multi-algorithm Solver for Dynamic Multi-Objective TSP (DMO-TSP)
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Introduction to Evolutionary Multiobjective Optimization
Multiobjective Optimization
Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Study of preference relations in many-objective optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Constrained many-objective optimization: a way forward
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Ranking Methods for Many-Objective Optimization
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Multicriteria decision making (MCDM): a framework for research and applications
IEEE Computational Intelligence Magazine
A grid-based fitness strategy for evolutionary many-objective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Enhancing diversity for average ranking method in evolutionary many-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
IEEE Transactions on Evolutionary Computation
Information and Software Technology
Preference-driven co-evolutionary algorithms show promise for many-objective optimisation
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Evolution of architectural floor plans
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Multi-objective probability collectives
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Alleviate the hypervolume degeneration problem of NSGA-II
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
An approach based on grid-value for selection of parents in multi-objective genetic algorithm
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Visualising many-objective populations
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Programming and evolving physical self-assembling systems in three dimensions
Natural Computing: an international journal
Computers and Operations Research
On the effect of selection and archiving operators in many-objective particle swarm optimisation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Refined ranking relations for multi objective optimization andapplication to P-ACO
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Advances in Engineering Software
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The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much evidence of their worth in practical scenarios. An urgent emerging issue is that the favoured EMO algorithms scale poorly when problems have "many" (e.g. five or more) objectives. One of the chief reasons for this is believed to be that, in many-objective EMO search, populations are likely to be largely composed of nondominated solutions. In turn, this means that the commonly-used algorithms cannot distinguish between these for selective purposes. However, there are methods that can be used validly to rank points in a nondominated set, and may therefore usefully underpin selection in EMO search. Here we discuss and compare several such methods. Our main finding is that simple variants of the often-overlooked "Average Ranking" strategy usually outperform other methods tested, covering problems with 5-20 objectives and differing amounts of inter-objective correlation.