Genetic Algorithms in Noisy Environments
Machine Learning
Dynamic Control of Genetic Algorithms in a Noisy Environment
Proceedings of the 5th International Conference on Genetic Algorithms
Pareto-Front Exploration with Uncertain Objectives
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Evolutionary Multi-objective Ranking with Uncertainty and Noise
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Fitness inheritance for noisy evolutionary multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Scheduling of genetic algorithms in a noisy environment
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Evolutionary multiobjective optimization in noisy problem environments
Journal of Heuristics
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
Deriving a robust policy for container stacking using a noise-tolerant genetic algorithm
Proceedings of the 2012 ACM Research in Applied Computation Symposium
A non-parametric statistical dominance operator for noisy multiobjective optimization
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Proceedings of the 2013 Research in Adaptive and Convergent Systems
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Objective evaluation is subject to noise in many real-world problems. The noise can deteriorate the performance of multi-objective evolutionary algorithms, by misleading the population to a local optimum and reducing the convergence rate. This paper proposes three novel noise handling techniques: accumulative sampling, a new ranking method, and a different selection scheme for recombination. The accumulative sampling is basically a kind of dynamic resampling, but it does not explicitly decide the number of samples. Instead, it repeatedly takes additional samples of objectives for the solutions in the archive at every generation, and updates the estimated objectives using all the accumulated samples. The new ranking method combines probabilistic Pareto rank and crowding distance into a single aggregated value to promote the diversity in the archive. Finally, the fitness function and selection method used for recombination are made different from those for the archive to accelerate the convergence rate. Experiments on various benchmark problems have shown that the algorithm adopting all these features performs better than other MOEAs in various performance metrics.