Performance Measures for Dynamic Environments
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
PSFGA: parallel processing and evolutionary computation for multiobjective optimisation
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Performance measures and particle swarm methods for dynamic multi-objective optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Multi-swarm co-evolutionary paradigm for dynamic multi-objective optimisation problems
International Journal of Intelligent Information and Database Systems
Hi-index | 0.00 |
As most of the performance measures proposed for dynamic optimization algorithms in the literature are only for single objective problems, we propose new measures for dynamic multi-objective problems. Specifically, we give new measures for those problems in which the Pareto fronts are unknown. As these problems are the most common in the industry, our proposed measures constitute an important contribution in order to promote further research on these problems.