Quality Assessment of Pareto Set Approximations
Multiobjective Optimization
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Online convergence detection for multiobjective aerodynamic applications
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Empirical comparison of MOPSO methods: guide selection and diversity preservation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Pareto-dominance in noisy environments
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Population-based ant colony optimisation for multi-objective function optimisation
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Privacy-preserving data mining: A feature set partitioning approach
Information Sciences: an International Journal
Evolving agent behavior in multiobjective domains using fitness-based shaping
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolutionary multi-objective optimization and decision making for selective laser sintering
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Graph partitioning by multi-objective real-valued metaheuristics: A comparative study
Applied Soft Computing
On the computation of the empirical attainment function
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Hyperheuristic encoding scheme for multi-objective guillotine cutting problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Multi-objective optimization of dynamic memory managers using grammatical evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Using multi-objective metaheuristics to solve the software project scheduling problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A multi-objective approach for the 2D guillotine cutting stock problem
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Improving robustness of multiple-objective genetic programming for object detection
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Eliminating useless object detectors evolved in multiple-objective genetic programming
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
A novel multiobjective formulation of the robust software project scheduling problem
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Robust solutions for the software project scheduling problem: a preliminary analysis
International Journal of Metaheuristics
Evolutionary algorithms for the multi-objective test data generation problem
Software—Practice & Experience
Comparison of design concepts in multi-criteria decision-making using level diagrams
Information Sciences: an International Journal
Proceedings of the Winter Simulation Conference
An approach to visualizing the 3D empirical attainment function
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Computational Optimization and Applications
Hi-index | 0.00 |
When evaluating the performance of a stochastic optimizer it is sometimes desirable to express peformance in terms of the quality attained in a certain fraction of sample runs. For example, the sample median quality is the best estimator of what one would expect to achieve in 50% of runs, and similarly for other quantiles. In multiobjective optimization, the notion still applies but the outcome of a run is measured not as a scalar (i.e. the cost of the best solution), but as an attainment surface in the k-dimensional space where k is the number of objects). In this paper we report an algorithm that can be conveniently used to plot summary attainment surfaces in any number of dimensions (though it is particularly suited for three). A summary attainment surface is defined as the union of all tightest goals that have been attained (independently) in precisely s of the runs of a sample of \eta runs, for any s \in 1...\eta and for any k We also discuss the computational complexity of the algorithm and give some examples of its use. C code for the algorithm is available from the author.